Optical coherence tomography apparatus, control method for optical coherence tomography apparatus, and computer readable storage medium

ABSTRACT

The optical coherence tomography apparatus is an optical coherence tomography apparatus that obtains a tomographic image of an eye to be examined by using combined light obtained by combining (a) return light from the eye to be examined irradiated with measurement light and (b) reference light, the optical coherence tomography apparatus including an optical path length difference changing unit arranged to change an optical path length difference between the measurement light and the reference light, a driving unit arranged to drive the optical path length difference changing unit, a determining unit configured to determine, using a learned model, a driving amount of the driving unit from the obtained tomographic image, and a controlling unit configured to control the driving unit using the determined driving amount.

BACKGROUND Field of the Disclosure

The disclosure according to the present specification relates to anoptical coherence tomography apparatus, a control method for an opticalcoherence tomography apparatus, and a computer readable storage medium.

Description of the Related Art

Apparatuses for optical coherence tomography (OCT), which usesinterference between low coherence light beams to obtain a tomographicimage, (OCT apparatuses) have been in practical use. The OCT apparatusescan obtain a tomographic image at a resolution on the order of awavelength of light entering an object to be examined, and thus thetomographic image of the object to be examined can be obtained at a highresolution. The OCT apparatuses are useful as ophthalmologicalapparatuses for obtaining a tomographic image particularly of a retinalocated at a fundus.

A configuration of OCT is, for example, time domain OCT (TD—OCT), whichis a combination of a wide-band light source and a Michelsoninterferometer. This is configured such that a coherence gate on which areference mirror is mounted is driven at a constant speed, and lightinterfered with backscattered light obtained by a measurement arm ismeasured, by which a reflected light intensity distribution in a depthdirection is obtained. It is however difficult for such TD—OCT to obtainan image at high speed because the TD—OCT requires mechanical scanning.Hence, spectral domain OCT (SD-OCT), which uses a wide-band light sourceand obtains an interference signal with a spectroscope, and swept sourceOCT (SS-OCT), which uses a high-speed wavelength-sweeping light sourceto perform spectroscopy with time, have been developed as a method forobtaining an image at higher speed, which have enabled tomographicimages of wider angles of view to be obtained.

Such OCT apparatuses detect an interference signal of measurement lightapplied to an eye to be examined and reference light applied to areference mirror. To image an eyes to be examined with an OCT apparatus,an optical path length difference between the measurement light and thereference light needs to be adjusted with accuracy. In this regard,Japanese Patent Application Laid-Open No. 2008-154939 describes anoptical image measurement apparatus that determines a position of aretina in a tomographic image and adjusts an optical path lengthdifference between measurement light and reference light based on thedetermined position so that the retina is located at a predeterminedposition.

A method for adjusting an optical path length difference betweenmeasurement light and reference light based on a position of a retinadetermined in a tomographic image involves a problem in that theadjustment may not be performed accurately depending on a shape of aneye to be examined. For example, in a case where a retina has a largebend due to myopia, a peripheral portion of the retina may protrude froma tomographic image to disappear or may be displayed being turned up ordown even when a center portion of the retina is aligned with a positionin the tomographic image that allows observation (predeterminedposition). This problem appears prominently particularly in an OCTapparatus having a wide imaging view angle.

SUMMARY

Hence, an objective of the disclosure of the present specification is toadjust, in optical coherence tomography, an optical path lengthdifference between measurement light and reference light with highaccuracy.

An optical coherence tomography apparatus according to an embodiment ofthe disclosure of the present specification is an optical coherencetomography apparatus that obtains a tomographic image of an eye to beexamined by using combined light obtained by combining (a) return lightfrom the eye to be examined irradiated with measurement light and (b)reference light, the optical coherence tomography apparatus including anoptical path length difference changing unit arranged to change anoptical path length difference between the measurement light and thereference light, a driving unit arranged to drive the optical pathlength difference changing unit, a determining unit configured todetermine, using a learned model, a driving amount of the driving unitfrom the obtained tomographic image, and a controlling unit configuredto control the driving unit using the determined driving amount.

Further features of the present invention will become apparent from thefollowing description of exemplary embodiments with reference to theattached drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of a schematic configuration of an opticalcoherence tomography apparatus according to Embodiment 1.

FIG. 2 illustrates an example of an imaging screen according toEmbodiment 1.

FIG. 3 illustrates an example of a flowchart of position adjustmentprocessing of a coherence gate according to Embodiment 1.

FIG. 4 illustrates an example of a flowchart of fine adjustmentprocessing of coherence gate according to Embodiment 1.

FIG. 5 illustrates an example of a schematic diagram of a neural networkmodel according to Embodiment 1.

FIG. 6 illustrates an example of training data according to Embodiment1.

FIG. 7 illustrates an example of a course of interpolation processing ona tomographic image according to Embodiment 1.

FIG. 8 illustrates an example of a course of shifting processing on atomographic image according to Embodiment 1.

FIG. 9 illustrates an example of a method for generating a pseudo myopiaimage according to Embodiment 1.

FIG. 10 illustrates an example of processing for extracting part of atomographic image according to Embodiment 1.

FIG. 11 illustrates an example of processing using a mirror-image partaccording to Embodiment 1.

FIG. 12 illustrates an example of offsetting processing according toEmbodiment 1.

FIG. 13 illustrates an example of a flowchart of tracking processingaccording to Embodiment 2.

FIG. 14 illustrates an example of an SLO image and a plurality oftomographic images according to Modification 1.

FIG. 15 illustrates an example of a plurality of tomographic imagesaccording to Modification 2.

FIG. 16 illustrates an example of changes in estimated distanceaccording to Modification 2.

FIG. 17 illustrates an example of a flowchart of correction processingof a coherence gate according to Modification 5.

FIG. 18 illustrates an example of tomographic images of a wide angle ofview and a narrow angle of view according to Modification 6.

DESCRIPTION OF THE EMBODIMENTS

Preferred embodiments of the present invention will now be described indetail in accordance with the accompanying drawings.

Note that dimensions, materials, shapes, relative positions ofconstituent components, and the like to be described in the followingembodiments are optional and can be modified in conformance with aconfiguration of or various conditions for an apparatus to which thepresent disclosure is to be applied. In addition, the same referencecharacters will be used in the drawings for indicating the same elementsor functionally similar elements. Note that constituent components,members, and part of processing in the drawings that are not importantin describing may not be illustrated.

Note that a machine learning model herein refers to a learning modelprovided by a machine learning algorithm. Concrete algorithms formachine learning include those for nearest neighbor methods, naive Bayesmethods, decision trees, support vector machines. The algorithms alsoinclude those for deep learning, which uses a neural network to generatefeature quantities for learning and connection weight coefficients onits own. In addition, examples of an algorithm using a decision treeinclude those for methods using gradient boosting, such as LightGBM andXGBoost. Of the algorithms described above, available one can be usedand applied to the following embodiments and modifications asappropriate. Supervisory data refers to training data and includes apair of input data and output data (ground truth).

A learned model refers to a model that is a machine learning modelaccording to any machine learning algorithm such as deep learning andhas been trained with appropriate supervisory data (training data) inadvance. Although the learned model is obtained in advance by using theappropriate training data, a possibility of further training is noteliminated, and the learned model can be subjected to additionaltraining. The addition training can be performed after the apparatus isinstalled at a service space.

Herein, a depth direction of a subject is defined as a Z direction, adirection perpendicular to the Z direction is defined as an X direction,and a direction perpendicular to the Z direction and the X direction isdefined as a Y direction.

Embodiments 1 and 2 will be described below as exemplary embodiments.Embodiment 1 will describe an example of performing alignment processingon an optical path length difference changing unit with a learned model,and Embodiment 2 will describe an example of performing trackingprocessing on the optical path length difference changing unit with alearned model.

Embodiment 1

As an example of an ophthalmic photography apparatus according toEmbodiment 1 of the present disclosure, an optical coherence tomographyapparatus will be described below with reference to FIG. 1 to FIG. 12.The optical coherence tomography apparatus in the present embodiment hasa configuration of SS-OCT. The configuration of the optical coherencetomography apparatus is however not limited to SS-OCT and may be ofSD-OCT.

FIG. 1 is a diagram illustrating a configuration example of an opticalcoherence tomography apparatus (OCT apparatus) according to the presentembodiment. The OCT apparatus includes a wavelength-sweeping lightsource 11 that sweeps a frequency of light emitted, an OCT interferenceunit 20 that generates interfered light, a detection unit 30 thatdetects the interfered light, and a controlling unit 40 that obtains,based on the interfered light, information on fundus of an eye to beexamined being a subject 120. The OCT apparatus further includes ameasurement arm 50 and a reference arm 60. Additionally, the OCTapparatus includes an SLO light source 12 for a scanning laserophthalmoscope (SLO) and includes an SLO optical system 80 for obtainingreflected light from the fundus and an anterior ocular segment imagingunit 90.

<Configuration of OCT Measurement System>

The OCT interference unit 20 includes couplers 21 and 22. First, thecoupler 21 splits light emitted from the wavelength-sweeping lightsource 11 into measurement light with which the fundus is to beirradiated and reference light. In the present embodiment, a split ratiois about 2:8, measurement light:reference light=2:8. Note that the splitratio may be set optionally according to a desired configuration.

The measurement light is applied to the fundus being the subject 120 viathe measurement arm 50. More specifically, entering the measurement arm50, the irradiation light is adjusted in its polarized state by apolarization controller 51 and then emitted as spatial light from acollimator 52. The irradiation light then passes through lenses 53 and54, an X scanner 55, a Y scanner 56, a dichroic mirror 103, a lens 57, afocus lens 58, a dichroic mirror 105, and an objective lens 106 and isapplied to the fundus of the subject 120.

The X scanner 55 and the Y scanner 56 are scanning units each having afunction of scanning the fundus with the irradiation light. The scanningunits change a position of irradiation of the fundus with themeasurement light.

The dichroic mirror 103 has characteristics of reflecting light havingwavelengths of 1000 nm to 1100 nm and allowing light of the otherwavelengths to pass therethrough. The dichroic mirror 105 hascharacteristics of reflecting light having wavelengths of 820 nm to 920nm and allowing light of the other wavelengths to pass therethrough.

The focus lens 58 is fixed to a focus stage 59 and is movable in anoptical axis direction by drive of the focus stage 59 by the controllingunit 40. By moving the focus lens 58, a focal position of themeasurement light can be changed.

Backscattered light (reflected light) from the fundus of the subject 120travels along the above-described optical path again and is emitted fromthe measurement arm 50. The reflected light that has exited from themeasurement arm 50 enters the coupler 22 via the coupler 21. Accordingto the aforementioned split ratio of the coupler 21, 80% of thereflected light (return light from the fundus) that has passed throughthe coupler 21 is directed to the coupler 22.

Meanwhile, the reference light enters the coupler 22 via the referencearm 60. More specifically, entering the reference arm 60, the referencelight is adjusted in its polarized state by a polarization controller 61and then emitted as spatial light from a collimator 62. The referencelight then passes through a dispersion compensation glass 63, acoherence gate 64, and a dispersion controlling prism pair 66, enters anoptical fiber via a collimator lens 67, is emitted from the referencearm 60, and enters the coupler 22.

The coherence gate 64 is an example of an optical path length differencechanging unit that changes a difference in optical path length betweenthe measurement light and the reference light by changing an opticalpath length of the reference light. Note that the optical path lengthdifference changing unit may be one that changes, for example, anoptical path length of the measurement light. The coherence gate 64includes, for example, a retroreflector prism. The coherence gate 64 mayinclude at least two or more mirrors or may include a single mirror. Thecoherence gate 64 is capable of being driven in an optical axisdirection by a driving motor 65. Note that the driving motor 65 is anexample of a driving unit that drives the optical path length differencechanging unit and may include any known motor such as a stepping motorand a DC motor. In addition to the driving motor 65, the driving unitmay include a stage or the like.

In the coupler 22, the reflected light from the subject 120 havingpassed through the measurement arm 50 interferes with the light havingpassed through the reference arm 60. The interfered light is detected bythe detection unit 30. The detection unit 30 includes a differentialdetector 31 and an A/D converter 32.

Immediately after the interfered light is generated by the coupler 22,the interfered light is split into interfered light beams, and in thedetection unit 30, the interfered light beams are detected by thedifferential detector 31 and converted into an OCT interference signalin a form of an electric signal. The A/D converter 32 converts the OCTinterference signal in a form of an electric signal, which is convertedinto by the differential detector 31, into a digital signal. Here, inthe OCT apparatus illustrated in FIG. 1, the interfered light beams aresampled for every optical frequency (wavenumber) based on a k-clocksignal transmitted by a k-clock generating unit that is built in thewavelength-sweeping light source 11. The A/D converter 32 outputs thedigital signal of the OCT interference signal converted into to thecontrolling unit 40. The controlling unit 40 is capable of obtaining,based on the digital signal of the OCT interference signal output fromthe A/D converter 32, information about a section of the subject 120 andgenerating a tomographic image.

The above is a process for obtaining information about a section of thesubject 120 at one point of the subject 120, and obtaining informationabout a section of the subject 120 in a depth direction in this manneris called A-scan. In addition, obtaining information about a section ofthe subject 120 in a direction perpendicular to that of the A-scan, thatis, information about a two-dimensional image, is called B-scan.Moreover, obtaining information about a section of the subject 120 in adirection perpendicular to scanning directions of both the A-scan andthe B-scan is called C-scan. In a case where a two-dimensional rasterscan is performed on a fundus to obtain a three-dimensional tomographicimage, a scanning direction of a fast scan is called B-scan direction,and a scanning direction of a slow scan that is performed on B-scansarranged in a direction perpendicular to the B-scan direction is calledC-scan direction. Performing an A-scan and a B-scan produces atwo-dimensional tomographic image, and performing an A-scan, a B-scan,and a C-scan produces a three-dimensional tomographic image. The B-scanand the C-scan are performed by scanning the fundus with the measurementlight by using the X scanner 55 and the Y scanner 56 described above.

Note that the X scanner 55 and the Y scanner 56 include respectivedeflection mirrors that are disposed such that rotation axes of thedeflection mirrors are perpendicular to each other. The X scanner 55performs a scan in an X-axis direction, and the Y scanner 56 performs ascan in a Y-axis direction. The X-axis direction and the Y-axisdirection are both perpendicular to an eye axis direction of an eyeballand are perpendicular to each other. In addition, linear scan directionsof the B-scan and the C-scan each may not match the X-axis direction orthe Y-axis direction. For this reason, the linear scan directions of theB-scan and the C-scan can be determined as appropriate according to atwo-dimensional tomographic image or three-dimensional tomographic imageto be captured. In the present embodiment, the scanning unit of the OCToptical system includes the X scanner 55 and the Y scanner 56; however,the OCT optical system may include, for example, a MEMS mirror, whichcan deflect light in a direction in a two-dimensional manner singly.

<Configuration of SLO Measurement System>

The light that has exited from the SLO light source 12 is applied to thefundus via the SLO optical system 80. More specifically, the light thathas entered the SLO optical system 80 is emitted as parallel rays into aspace from a collimator 81. The light then passes through a hole portionof a hole mirror 101, via a lens 82, an X scanner 83, lenses 84 and 85,and a Y scanner 86, reaches a dichroic mirror 102, and is reflected bythe dichroic mirror 102. The X scanner 83 and the Y scanner 86 are anexample of a scanning unit for SLO. The scanning unit for SLO mayinclude the X scanner 55 and the Y scanner 56 for OCT as an XY scanningunit shared by the SLO optical system 80 and the OCT optical system. Thedichroic mirror 102 has characteristics of reflecting light havingwavelengths of 760 nm to 800 nm and allowing light of the otherwavelengths to pass therethrough. The light reflected by the dichroicmirror 102 passes along the same optical path as the optical path of themeasurement light for OCT and reaches the fundus of the subject 120.

The measurement light for SLO applied to the fundus is reflected andscattered by the fundus, travels along the above-described optical path,reaches the hole mirror 101, and is reflected by the hole mirror 101.The light reflected by the hole mirror 101 passes through a lens 87 andreceived by an avalanche photodiode (hereinafter, abbreviated as APD)88, where the light is converted into an electric signal and output tothe controlling unit 40. The controlling unit 40 is capable ofgenerating, based on an SLO fundus signal output from the APD 88, an SLOimage, which is a fundus front image. Note that the signal output fromthe APD 88 may be output to the controlling unit 40 in a form of adigital signal by an A/D converter not illustrated.

Here, a position of the hole mirror 101 is conjugate to a pupil positionof the eye to be examined. As a result, regarding the measurement lightfor SLO applied to the fundus, light that passed through a peripheralportion of the pupil out of the light having been reflected andscattered by the fundus is reflected by the hole mirror 101.

In the present embodiment, the OCT apparatus includes the SLO opticalsystem 80 as a configuration for obtaining a fundus front image, whichhowever does not limit the configuration for obtaining a fundus frontimage. For example, the OCT apparatus may include a fundus photographysystem for obtaining a fundus photograph, and hereafter, processing withan SLO image may be substituted by processing with a fundus photograph.

<Configuration of Anterior Ocular Segment Measurement System>

The anterior ocular segment imaging unit 90 is used for imaging ananterior ocular segment of the subject 120 with an illumination lightsource 95 that includes an LED emitting illumination light having awavelength of 860 nm. The illumination light emitted from theillumination light source 95 is reflected by the anterior ocularsegment, passes through the objective lens 106, and reaches the dichroicmirror 105. The light reflected by the dichroic mirror 105 passesthrough lenses 91, 92 and 93 and is received by an anterior segmentcamera 94. The light received by the anterior segment camera 94 isconverted into an electric signal and output to the controlling unit 40.The controlling unit 40 is capable of generating an anterior segmentimage based on the signal output from the anterior segment camera 94.Note that the signal output from the anterior segment camera 94 may beoutput to the controlling unit 40 in a form of a digital signal by anA/D converter not illustrated.

<Internal Fixation Lamp 110>

An internal fixation lamp 110 includes a display unit 111 for theinternal fixation lamp and a lens 112. In the present embodiment, as thedisplay unit 111 for the internal fixation lamp, one in which aplurality of light emitting diodes (LEDs) is arranged in a matrixpattern is used. Lighting positions of the light emitting diodes arechanged depending on a region to be imaged. Light from the display unit111 for the internal fixation lamp passes through the lens 112 and isdirected to the subject 120. Note that the light that has exited fromthe display unit 111 for the internal fixation lamp has a wavelength of520 nm, and on the display unit 111 for the internal fixation lamp, adesired preset pattern is displayed.

<Controlling Unit 40>

The controlling unit 40 performs signal processing on the OCTinterference signal converted into a digital signal to perform varioustypes of image processing such as generating an optical coherencetomographic image. Likewise, the controlling unit 40 is capable ofprocessing the SLO fundus signal output from the APD 88 to generate anSLO image. In addition, the controlling unit 40 is capable of processingthe signal output from the anterior segment camera 94 to generate ananterior segment image.

Based on a program, the controlling unit 40 controls drive mechanisms inthe OCT apparatus including the driving motor 65 for the coherence gate64. The controlling unit 40 therefore functions as an example of acontrolling unit that controls the driving unit.

The controlling unit 40 may be a computer built in (inside) the OCTapparatus or may be a separate (outside) computer to which the OCTapparatus is connected so as to be able to communicate with thecomputer. The controlling unit 40 may be, for example, a personalcomputer (PC); a desktop PC, a laptop PC, a tablet PC (portableinformation terminal) may be used. At this time, a communicationconnection between the controlling unit 40 and the ophthalmologicalequipment may be a connection made by wired communication or aconnection made by wireless communication. Note that a processor of thecomputer may be a central processing unit (CPU). The processor may be,for example, a micro processing unit (MPU), a graphical processing unit(GPU), or a field-programmable gate array (FPGA).

<Adjustment Flow>

Information on the fundus and the anterior ocular segment obtained asresults of the signal processing by the controlling unit 40 is displayedby a display unit 70. FIG. 2 illustrates an example of an imaging screen200 displayed on the display unit 70 at the time of imaging. In adisplay region 201 as an example of a display region of the imagingscreen 200, an anterior segment image 202, an SLO image 203, and atomographic image 206, which are generated by the controlling unit 40,are displayed.

With reference to the imaging screen 200 illustrated in FIG. 2, aprocedure of an adjustment process such as adjusting alignment forimaging with the OCT apparatus will be described below. First, based onthe anterior segment image 202, alignment of the measurement light ofthe OCT apparatus with the subject 120 in the optical axis direction isadjusted. The alignment may be adjusted manually by an examiner or maybe adjusted automatically by the controlling unit 40 recognizing animage of the anterior segment image 202.

Next, focus adjustment is performed so that the SLO image 203 becomesoptimum. The focus adjustment may be performed manually by the examinerwith a focus adjuster 205 or may be performed automatically by thecontrolling unit 40 based on the SLO image 203. In a case where thefocus adjuster 205 is used, the controlling unit 40 drives the focusstage 59 correspondingly to an operation of the focus adjuster 205 bythe examiner, moving the focus lens 58.

Next, an OCT scan area is set. The OCT scan area can be specified with,for example, a guide 204 displayed on the SLO image 203. The guide canbe set to have any size and shape and to be at any position; forexample, a 23 mm×20 mm quadrilateral, a radial pattern inscribed in acircle having a diameter of 10 mm, a 10-mm line pattern can be selectedas the guide. The controlling unit 40 causes the display unit 70 todisplay, as the tomographic image 206, a given tomographic image that isobtained within the scan area specified with the guide 204.

Lastly, positional adjustment (alignment) of the coherence gate 64 isperformed such that the tomographic image 206 becomes optimum. Anoptimal position for the coherence gate 64 differs between a vitreousbody mode, which uses a normal image in which a DC component of theinterference signal appears in an upper part of a tomographic image, anda choroid mode, which uses a reverse image in which the DC componentsappear in a lower part of a tomographic image. Due to characteristics ofcoherence, in the vitreous body mode, the tomographic image is seenbrighter at an upper part of the image, which is favorable for observingparticularly a region on a vitreous body side of a retina. In contrast,in the choroid mode, the tomographic image is seen brighter at a lowerpart of the image, which is favorable for observing particularly aregion on a choroid side of the retina. For each imaging, the examinercan specify one of the vitreous body mode and the choroid mode on theimaging screen or the like. Alternatively, before imaging, the examinermay set one of the vitreous body mode and the choroid mode together withan imaging condition such as a scan angle. A position of the coherencegate 64 is adjusted based on whether the mode is the vitreous body modeor the choroid mode.

With reference to FIG. 3, position adjustment processing of thecoherence gate 64 according to the present embodiment will be describedbelow for each step. FIG. 3 is a flowchart illustrating operations inthe position adjustment processing of the coherence gate according tothe present embodiment.

As the position adjustment processing of the coherence gate is started,the processing proceeds to step S31. In step S31, the controlling unit40 roughly estimates an optimal position (target position) for thecoherence gate 64 (rough estimation) based on a result of focusadjustment with the SLO image 203. The rough estimation is performed by,for example, using a conversion formula that is obtained by conductingregression analysis of relations between results of focus adjustmentusing SLO images performed on many eyes to be examined and optimalpositions of the coherence gate 64. By performing such a process, theoptimal position for the coherence gate 64 can be roughly estimatedbecause there is a correlation between a diopter scale of an eye to beexamined and an optimal position for the coherence gate 64. Note thathow to perform the rough estimation is not limited to this; for example,the rough estimation may be performed based on a result of focusadjustment with the anterior segment image 202. Alternatively, forexample, the rough estimation may be performed based on a result ofobtaining a fundus photograph and performing focus adjustment by animage plane phase difference method or the like. Alternatively, therough estimation may be performed by substituting an initial set valuefor any one of various types of focusing into the conversion formula. Asthe initial set value used here, a representative value such as a focusvalue optically calculated for an average eye to be examined and anaverage value resulting from focus adjustment performed on many eyes tobe examined can be used. The optimal position roughly estimated will behereinafter called a rough-estimated position. The rough-estimatedposition may be a representative value such as a position of thecoherence gate 64 optically calculated for an average eye to be examinedand an average value resulting from adjustments of the coherence gate 64performed on many eyes to be examined can be used.

In step S32, the controlling unit 40 drives the coherence gate 64 froman initial position of the coherence gate 64 to the rough-estimatedposition. The initial position of the coherence gate 64 may be an end ofa range within which the coherence gate 64 can be driven. Alternatively,the initial position may be a center position of the range within whichthe coherence gate 64 can be driven, a position resulting fromadjustments of the coherence gate 64 in past examinations, or any otherposition. Here, a driving speed of the coherence gate 64 may be set at apossible maximum speed. A tomographic image obtained when the coherencegate 64 is moved to the rough-estimated position may be an imageobtained by a B-scan (B-scan image) at an observation target position ormay be a B-scan image at a representative position such that a positionthat lies at a center of an imaging view angle.

Next, in step S33, the controlling unit 40 performs fine adjustment onthe position of the coherence gate 64. With reference to FIG. 4, fineadjustment processing of coherence gate in the present embodiment willbe described. The following fine adjustment processing of coherence gateis performed by the controlling unit 40.

As the fine adjustment processing of coherence gate is started, theprocessing proceeds to step S41. In step S41, the controlling unit 40sets a number i of trials at zero.

Next, in step S42, the controlling unit 40 obtains a tomographic image.The tomographic image obtained here may be a B-scan image at a specific,designated XY position or may be a B-scan image at a representative XYposition such as one that lies on a center line of (line passing acenter of) an imaging view angle. The tomographic image obtained in theadjustment processing will be hereinafter called preview image. Thepreview image may be a low-resolution image generated by dropping datafrom a tomographic image obtained by imaging.

The controlling unit 40 determines an image quality of the tomographicimage obtained in step S42, and when the image quality is lower than athreshold value, the controlling unit 40 may not use the tomographicimage but may obtain a tomographic image of a next frame. This canprevent a malfunction caused by a blink of a subject that specificallydarkens a tomographic image. As an evaluation index for the imagequality, for example, an intensity value of the tomographic image in agiven range can be used. Alternatively, a variance, a standarddeviation, a skewness, a kurtosis, or the like of a frequencydistribution (histogram distribution) of the image may be used as theevaluation index for the image quality. When the subject blinks, pixelshaving high intensity values are reduced, and thus these values vary.Using the frequency distribution for the evaluation index allows theimage quality to be determined irrespective of a difference inbrightness of the image as a whole.

Next, in step S43, the controlling unit 40 uses a learned model toobtain an estimated distance of the coherence gate 64 from the obtainedtomographic image. The estimated distance here is an estimated distancefrom a current position to the optimal position for the coherence gate64. The estimated distance is a signed value, and the sign indicates adirection of driving the coherence gate 64. Here, a process performed instep S43 will be described in detail by way of an example in which aneural network model is used as the learned model.

FIG. 5 illustrates an example of a schematic diagram of a neural networkmodel according to the present embodiment. According to the modelschematic diagram illustrated in FIG. 5, the neural network is designedto output an estimated distance (a distance from a current position toan optimal position) Δz of the coherence gate 64 from image informationthat is input as two-dimensional pixel array data on a tomographicimage. The estimated distance Δz to be output is based on contentlearned through a machine learning process (deep learning), and theneural network according to the present embodiment learns relationsbetween information about shapes of retinae included in tomographicimages and estimated distances Δz.

The model used in the present embodiment may be a regression model inwhich input data is a tomographic image and output data is an estimateddistance of the coherence gate 64. The model used in the presentembodiment may be a classification model in which input data is atomographic image and output data is one of classes at a plurality oflevels into which an estimated distance of the coherence gate 64 isdivided.

The input data may be changed in size from that of a tomographic imageobtained by the OCT apparatus in view of a load of the learning or theestimation. A method for changing the size may be a method in which, forexample, the size is reduced by dropping an A-scan data item from thetomographic image every other A-scan data item or every plurality ofA-scan data items. Alternatively, the method for changing the size maybe a method in which the size is reduced by dropping data from eachA-scan data item of the tomographic image. Alternatively, a region beingpart of the tomographic image may be extracted as illustrated in FIG. 10to be described later. Note that the learned model used in the presentembodiment is stored in the controlling unit 40.

The neural network according to the basic specifications described abovecan be configured as a convolutional neural network (CNN) which enablesflexible pattern recognition, for example, by forming an intermediatelayer after an input layer as a combination of what one calls aconvolution layer and a pooling layer. In addition, for example, a layerclosest to an output layer can be formed as a fully connected layer,which is suitable for optimum value operations.

The neural network according to the present embodiment can be trained byany one of what one calls supervised learning and reinforcementlearning. Here, how to train the neural network according to the presentembodiment will be described. An example of how the controlling unit 40performs the training of the learned model will be described below, butnote that the training may be performed by a training unit notillustrated separate from the controlling unit 40 or may be performed bya separate apparatus.

In a case where the supervised learning is performed, tomographic imagescan be used as input data, and estimated distances of the coherence gate64 can be used as output data. With reference to FIG. 6, training datafor performing the supervised learning will be described below. FIG. 6is a diagram for describing tomographic images used for the learning andillustrates an example of tomographic images of an eye to be examinedthat are obtained in a state where the coherence gate 64 is located at aplurality of positions.

The tomographic images illustrated as (a) to (e) in FIG. 6 can beobtained by, for example, sequentially recording the tomographic imagesand imaging time points of the tomographic images while the coherencegate 64 is driven across a range where the coherence gate 64 can bedriven. The tomographic images and corresponding driving distances ofthe coherence gate 64 can be obtained in association with each otherfrom a driving speed of the driving motor 65 for the coherence gate 64and a time interval between every tomographic image obtaining. Thedriving speed of the driving motor 65 for the coherence gate 64 may becalculated, for example, in an averaging manner by dividing a distancefrom a driving start position to a driving end position by a drivingduration.

The image (a) in FIG. 6 illustrates a case where a retina in thetomographic image deviates downward from an optimal position for theretina because the coherence gate 64 deviates from the optimal positionfor the coherence gate 64. Since the coherence gate 64 deviates from theoptimal position, a center portion of the retina in the tomographicimage is turned up. This is unsuitable for observation and diagnosis.

The image (b) in FIG. 6 illustrates an example of a tomographic imagewhen the retina is located at the optimal position because the coherencegate 64 is driven in a direction that causes the retina to be seen in anupper part of the tomographic image than in the image (a) in FIG. 6. Theoptimal position for the coherence gate 64 that is a target for theadjustment of an optical path length difference between the measurementlight and the reference light (target position) can be set or adjustedby an examiner so as to allow the entire retina to be seen in atomographic image or so as to facilitate observation of a noteworthyregion. In addition, in a case of imaging in the vitreous body mode, inwhich a DC component of an interference signal appears in an upper partof the tomographic image, an image corresponding to the interferencesignal is seen brighter at an upper part of the tomographic image. Theoptimal position for the coherence gate 64 thus may be at, for example,a position that causes the retina to be seen in the tomographic image ata part as up as possible to the extent that the retina is not turneddown or, in view of the possibility that the coherence gate 64 couldmove from the position during imaging, a position that causes the retinato be seen lower than the part by a margin. In addition, at a peripheralportion outside a center portion of a bend of the retina, the retina isseen upper in the tomographic image than at the center portion of thebend of the retina. In view of this fact, in a case where the positionof the coherence gate 64 is adjusted by referring to a tomographic imageat the center portion of the bend of the retina, the optimal positionfor the coherence gate 64 may be set at a position that causes theretina to be seen lower by this margin. Alternatively, the optimalposition may be set on a rule basis based on a representative point suchas one in the center portion and one in the peripheral portion of theretina. At this time, a region having high viewability, such as aboundary between retinal pigment epithelial cells (RPE) and a Bruchmembrane (BM), may be used as an index. For easiness of making an indexcommon to a plurality of imaging view angles, a lowermost portion of theretina in the image may be used as the index. In contrast, in a case ofimaging in the choroid mode, in which a DC component of an interferencesignal appears in a lower part of the tomographic image, an imagecorresponding to the interference signal is seen brighter at a lowerpart of the tomographic image. The optimal position for the coherencegate 64 in this case thus may be set at, for example, a position thatcauses the retina to be seen in the tomographic image at a part as lowas possible to the extent that the retina is not turned up or a positionthat causes the retina to be seen upper than the part by a margin inview of the possibility that the coherence gate 64 could move from theposition during imaging. The optimal position may be set on a rule basisas in the vitreous body mode described above; for example, the lowermostportion of the retina in the image may be used as the index.

In the supervised learning according to the present embodiment, anestimated distance of the coherence gate 64 that is output for atomographic image at the optimal position is assumed to be zero toperform the learning. In creating this training data, an estimateddistance of a tomographic image obtained at a position closest to theoptimal position, out of obtained tomographic images, may be labeledwith zero. Alternatively, for a tomographic image obtained at a positionclosest to the optimal position, a distance from the position to theoptimal position may be estimated by shifting processing or the like,and the tomographic image may be labeled with the distance. At thistime, other tomographic images may be labeled based on a time intervalfor a tomographic image obtained at a position closest to the optimalposition and the driving speed of the driving motor 65. Alternatively,an examiner may specify an offset amount for the optimal position forthe coherence gate 64 on a display screen for each imaging.

As a tomographic image used for the estimation, one resulting fromimaging in a scanning direction that provides a sharp bend of the retinamay be used. This reduces a risk that when the position of the coherencegate 64 is adjusted from a tomographic image obtained in a givenscanning direction, the retina is seen turned up or down in tomographicimages obtained in other scanning directions. In general, a bend of aretina is sharper in a horizontal direction than in a verticaldirection, and thus the horizontal direction may be set as an example ofthe scanning direction that provides a sharp bend of the retina.However, the scanning direction set here is not limited to thehorizontal direction, and any direction can be set as the scanningdirection.

In the present embodiment, how long a current position of the coherencegate 64 deviates from the position of the coherence gate 64 when thetomographic image illustrated in the image (b) in FIG. 6 is obtained ismeasured, and this deviation is learned as an estimated distance outputfor a current tomographic image. Note that, in a case of the image (a)in FIG. 6, since a center portion of the retina is seen turned up,adjusting the optical path length difference precisely is difficult forthe controlling unit 40 by a method of determining a position of the eyeto be examined from a Z coordinate of the center portion of the retina.

The image (c) in FIG. 6 illustrates a case where the retina in thetomographic image deviates upward from the retina in the image (b) inFIG. 6 because the coherence gate 64 deviates from the optimal position.This case is unsuitable for observation and diagnosis because theperipheral portion of the retina is seen turned down. In the presentembodiment, how long the position of the coherence gate 64 at this timedeviates from the position of the coherence gate 64 when the tomographicimage illustrated in the image (b) in FIG. 6 is obtained is measured,and this deviation is learned as an estimated distance output for thetomographic image illustrated in the image (c) in FIG. 6. In addition,in the case of the image (c) in FIG. 6, although the center portion ofthe retina is seen in the tomographic image at a position allowingobservation, the peripheral portion is turned down, and thus adjustingthe optical path length difference precisely is difficult for thecontrolling unit 40 by a method of determining a position of the eye tobe examined from a Z coordinate of the peripheral portion of the retina.

The image (d) in FIG. 6 illustrates a tomographic image of a case wherethe coherence gate 64 further deviates from the optimal position in thesame direction as that of the example illustrated in the image (c) inFIG. 6. This case is unsuitable for observation and diagnosis becausethe entire retina is seen turned up. In the present embodiment, how longthe position of the coherence gate 64 at this time deviates from theposition of the coherence gate 64 when the tomographic image illustratedin the image (b) in FIG. 6 is obtained is measured, and this deviationis learned as an estimated distance output for the tomographic imageillustrated in the image (d) in FIG. 6. In addition, in the case of theimage (d) in FIG. 6, the entire retina is seen in the tomographic imageat a position allowing observation but is turned down, and thusadjusting the optical path length difference precisely is difficult forthe controlling unit 40 by a method of determining a position of the eyeto be examined from a Z coordinate of a representative point such as onein the center portion.

The image (e) in FIG. 6 illustrates a tomographic image of a case wherethe coherence gate 64 further deviates from the optimal position in thesame direction as that of the example illustrated in the image (d) inFIG. 6. As a result of lowering the position of the retina in thetomographic image from the image (d) in FIG. 6, the peripheral portionof the retina is turned up, and the retina is seen in the samearrangement structure as that of the image (a) in FIG. 6. This case isunsuitable for observation and diagnosis because the center portion ofthe retina in the tomographic image is turned up. In the presentembodiment, how long the position of the coherence gate 64 at this timedeviates from the position of the coherence gate 64 when the tomographicimage illustrated in the image (b) in FIG. 6 is obtained is measured,and this deviation is learned as an estimated distance output for thetomographic image illustrated in the image (e) in FIG. 6. In addition,when there are images having similar retina structures that result fromgenerating retina signals that makes the retina seen turned up becausethe coherence gate 64 advances from the optimal position illustrated inthe image (b) in FIG. 6 to the image (a) and the image (e) in FIG. 6 inopposite directions to each other, at least one of the images may beexcluded from the training data. By not adding images having structuresalike to the training data, an error in inference can be decreased.

In addition, tomographic images that are darkened resulting from beingseparated enough from the DC component may be excluded from the trainingdata.

By obtaining combinations of tomographic images and estimated distancesdescribed above for many eyes to be examined, various training dataitems can be obtained. In addition, eyes to be examined used for thelearning may include healthy eyes and affected eyes. Using the learnedmodel obtained through learning with such various training data items,the controlling unit 40 can adjust the optical path length differencewith high accuracy for eyes to be examined having different shapes.

In addition, the training data may be increased by generatinginterpolated images from the tomographic images obtained as the trainingdata. An example of processing for generating an interpolated image willbe described with reference to FIG. 7. FIG. 7 is a diagram describing acourse of processing for interpolating a first tomographic image 711 anda second tomographic image 721 that are obtained back-to-back. Anexample of how the controlling unit 40 generating the training data willbe described below, but note that the training data may be generated bya training unit not illustrated separate from the controlling unit 40 ormay be performed by a separate apparatus.

First, in step S71, the controlling unit 40 performs binarizationprocessing on the first tomographic image 711 and the second tomographicimage 721 to generate a first binarized image 712 and a second binarizedimage 722, respectively. The binarization processing is performed suchthat an intensity value of a pixel having an intensity value equal to orhigher than a preset threshold value is set at one, and an intensityvalue of a pixel having an intensity value lower than the thresholdvalue is set at zero. As the threshold value, for example, a value thatis 0.3 times a maximum intensity value in a tomographic image can beset. A method for the binarization processing is not limited to theabove, and various methods such as an “Otsu's method” can be used. Afterthe binarization processing, skeletonization (thinning) processing maybe performed.

Next, in step S72, the controlling unit 40 calculates a Z coordinate zg1of a gravity center G1 of the first binarized image 712 and a Zcoordinate zg2 of a gravity center G2 of the second binarized image 722.The controlling unit 40 further calculates a difference Δzg between theZ coordinate zg1 and the Z coordinate zg2. Note that, in FIG. 7, adownward direction of the tomographic images is assumed to be a +Zdirection. Note that any well-known method may be used as a method forcalculating a Z coordinate of a gravity center of a binarized image.

Next, in step S73, the controlling unit 40 shifts the first tomographicimage 711 in the Z direction by +Δzg/3 to generate a first interpolatedimage 713. Likewise, the controlling unit 40 shifts the secondtomographic image 721 in the Z direction by −Δzg/3 to generate a secondinterpolated image 723.

Here, estimated distances as ground truth labels (ground truth) for thefirst tomographic image 711 and the second tomographic image 721 aredefined as estimated distances cz711 and cz721. At this time, anestimated distance cz713 corresponding to the first interpolated image713 and an estimated distance cz723 corresponding to the secondinterpolated image 723 may be determined by performing interpolationfrom the estimated distances cz711 and cz721 as shown by the followingFormula (1) and Formula (2).

cz713=cz711+(cz721−cz711)/3   Formula (1)

cz723=cz721−(cz721−cz711)/3   Formula (2)

In this manner, the controlling unit 40 generates two new interpolatedimages from the first tomographic image 711 and the second tomographicimage 721, by which the training data can be increased. By generatingthe interpolated images and performing the learning, intervals betweenground truth labels of estimated distances can be shortened, andinference resolution can be improved.

Although an example in which one interpolated image is generated fromeach of the first tomographic image 711 and the second tomographic image721 is described, two or more interpolated images may be generated fromeach tomographic image. For example, in a case where two interpolatedimages are to be generated from each of the first tomographic image 711and the second tomographic image 721, four interpolated images aregenerated in total by shifting the first tomographic image 711 in the Zdirection by +Δzg/5 to generate an interpolated image, shifting thefirst tomographic image 711 in the Z direction by +2Δzg/5 to generate aninterpolated image, shifting the second tomographic image 721 in the Zdirection by −Δzg/5 to generate an interpolated image, and shifting thesecond tomographic image 721 in the Z direction by −2×Δzg/5 to generatean interpolated image. Estimated distances as ground truth labels forthese interpolated images may be determined by performing interpolationfrom the estimated distances as ground truth labels corresponding to thefirst tomographic image 711 and the second tomographic image 721, asdescribed above.

Alternatively, the training data may be increased by generating newimages with a generative adversarial network (GAN).

In addition, the training data may be increased by a plurality of imagesby performing shifting processing on one tomographic image in aperpendicular direction (a depth direction of the retina). An example ofhow to perform the shifting processing will be described with referenceto FIG. 8. An image (a) in FIG. 8 illustrates an example of atomographic image serving as an original for the shifting processing(original image).

An image (b) in FIG. 8 illustrates an example of a background image. Thebackground image has the same size in pixels as that of the originalimage and is generated from pixels each having an intensity value thatis an intensity value of a background of the original image (pixels notin the retina). The intensity value of the pixels may be set at anaverage intensity value of the original image or an intensity value thatis determined to be of the background based on a frequency distributionof intensity values of the original image. For example, the intensityvalue may be set at an intensity value that is randomly selected fromintensity values of pixels accounting for 70% of the entire frequencydistribution from the darkest. The intensity value of the backgroundimage may be zero. A method for generating the background image is notlimited to the above, and the background image can be generated by anyappropriate method.

Next, generated background images are joined to the original image fromabove and below the original image to generate a joined image (an image(c) in FIG. 8). Then, the joined image is shifted by a predeterminedshifting amount (an image (d) in FIG. 8).

Next, portions of the retina that protrude from a region of the originalimage (inside region between upper and lower dotted lines in the image(d) in FIG. 8) may be displayed such that the portions are turned downat a boundary of the protrusion (a position of one of the up and downdotted lines in the image (d) in FIG. 8) (an image (e) in FIG. 8). Atthis time, the portions of the retina may be detected based onmagnitudes of intensity values. For example, when intensity values ofpixels protruding from the boundary are 1.5 times or greater intensityvalues of pixels at a position symmetric about the boundary, the pixelsprotruding from the boundary may be regarded as those of the retina.Alternatively, the portions of the retina may be detected in such amanner that whether the pixels protruding from the boundary aresufficient large with respect to an average value or a median of theintensity values of the background image. A method for detecting theportions of the retina is not limited to the above, and the portions canbe detected by any method.

In addition, with consideration given to the fact that a strength of asignal attenuates as the signal is separated from a DC component due todecrease in coherence, processing for attenuating intensity values basedon a distance of the signal from the DC component or the shifting amountmay be performed in the shifting processing. For example, in a casewhere an upper part of the tomographic image illustrated in the image(a) in FIG. 8 corresponding to the DC component, and in a case where thetomographic image is shifted upward and turned down as illustrated inthe image (d) in FIG. 8, an attenuation factor of a signal of a turnedportion may be set to be small or zero. Conversely, in a case where thetomographic image is shifted downward and turned up in an oppositemanner to that of the image (d) in FIG. 8, the attenuation factor of thesignal of a turned portion may be set to be large (an image (f) in FIG.8). Alternatively, with consideration given to the fact that intensityvalues level off when a signal intensity is high, the attenuation factormay be set based on magnitudes of original intensity values, such assetting the attenuation factor lower or setting no attenuation factorwhen the intensity values level off

By performing the shifting processing with a plurality of shiftingamounts being set, a plurality of shifted images is generated, and thetraining data can be increased. A relation between a driving amount ofthe coherence gate 64 and a shifting amount of the retina in thetomographic image is set based on optical specifications of theapparatus, a method for signal processing (method for setting the numberof data items), and the like. As an example, in a case where a depthrange of imaging tomographic images is 5 mm in terms of a distance inthe air, and the number of pixels of a tomographic image in alongitudinal direction is 100 pixels, the shifting amount is equivalentto a driving amount of 50 μm per pixel. A required adjustment resolutionfor the coherence gate 64 may be set by setting the required adjustmentresolution from the number of pixels of the tomographic image in thelongitudinal direction and converting the number of pixels into thedriving amount of the coherence gate 64. Furthermore, the shiftingamount may be set based on the required adjustment resolution of thecoherence gate 64, with which a plurality of tomographic images may begenerated in a pseudo manner. At this time, the learned model may beobtained from training data that in which input data is new tomographicimages obtained by shifting a tomographic image of an eye to be examinedin a depth direction of the eye to be examined by a predeterminedshifting amount and that includes the shifting amounts as ground truth.Note that such training data can be used not only for the coherence gate64 as an example of the optical path length difference changing unit,but also, for example, in a case where a driving amount of the opticalhead, the focus lens 58, or the like is used as the shifting amount.That is, the controlling unit 40 may use such learned model to determinea driving amount of the driving unit for driving an optical memberincluded in the optical coherence tomography apparatus (coherence gate64, optical head, focus lens 58, etc.) from obtained tomographic images.

In addition, trimming processing may be performed on an image of a wideangle of view to generate an image of a narrow angle of view in a pseudomanner, and the image of a narrow angle of view may be added to thetraining data. In the trimming processing, a center of the image may becut out, or a portion away from the center may be cut out. In addition,a tomographic image of a retina in myopia or high myopia may begenerated in a pseudo manner from one tomographic image and added to thetraining data.

A method for generating a pseudo myopia image will be described withreference to FIG. 9. An image (a) in FIG. 9 illustrates an example of atomographic image serving as an original for a pseudo myopia image(original image). First, the original image is enlarged in alongitudinal direction, by which an enlarged image is generated (animage (b) in FIG. 9). As an example of a method for generating theenlarged image, pixels on each column (A-scan) of the tomographic imagecan be moved upward. An amount of the upward movement may be determinedbased on, for example, a quadratic function of a lateral distance from acenter of the tomographic image to the column. A sharpness of a bend ofa shape of the retina can be adjusted with coefficients of the quadraticfunction, and images of a plurality of bend shapes may be created andused in the training data. In a case where an enlarged image isgenerated by moving pixels, pixels that become vacant due to themovement may be embedded with pixels having an average intensity valueof neighbor pixels or with pixels having a representative intensityvalue of a background region. Here, the background region may bedetermined based on, for example, a frequency distribution and may berandomly selected from pixels accounting for 70% of the entire frequencydistribution from the darkest.

Next, by the same method as that for the image (e) in FIG. 8, portionsprotruding from a region of the original image (portion above a dottedline illustrated in the image (b) in FIG. 9) may be displayed such thatthe portion is turned down at a boundary of the protrusion (dotted linein the image (b) in FIG. 9) (an image (c) in FIG. 9). In this manner, apseudo myopia image as illustrated in the image (c) in FIG. 9 can begenerated.

In addition, training data on pseudo myopia images may be increased byshifting processing. An example of the shifting processing will bedescribed below. First, the same background image as that illustrated inthe image (b) in FIG. 8 is generated. The background image is joined tothe enlarged image illustrated in the image (b) in FIG. 9, by which ajoined image is generated (an image (d) in FIG. 9). Next, the joinedimage is shifted by a predetermined shifting amount (an image (e) inFIG. 9). Subsequently, by the same method as that for the image (e) inFIG. 8, portions protruding from a region of the original image (portionabove a dotted line illustrated in the image (e) in FIG. 9) may bedisplayed such that the portion is turned down at a boundary of theprotrusion (upper dotted line in the image (e) in FIG. 9) (an image (f)in FIG. 9). In this manner, an image resulting from shifting a pseudomyopia image, as illustrated in the image (f) in FIG. 9 can begenerated. By setting a plurality of shifting amounts, training data onpseudo myopia images may be increased.

Similarly to the method for generating pseudo myopia images, tomographicimages of retinae having a dome shape and retinae having various shapesmay be generated in a pseudo manner by moving pixels on each column(A-scan) of a tomographic image and added to the training data. Inaddition, these pseudo images may be generated by referring tosegmentation information on a tomographic image of a retina serving asan original and modifying the segmentation information. In addition,tomographic images of retinal detachment may be generated in a pseudomanner by referring to the segmentation information.

Although a case where the training data is data on an eye to be examinedhas been described, the training data can be generated also by using amodel of an eye. In this case, a large number of training data items canbe generated more efficiently.

The learning according to the present embodiment can be performed withthe training data as described above by a backpropagation method, whichadjusts a weight coefficient of each edge connecting nodes so as toestablish a relationship between an input layer and an output layer ofeach neural network. Specifically, first, for an input data input intoan input layer of a machine learning model, output data output from anoutput layer of a neural network and errors between the output data andthe training data are obtained. Note that errors between the output datafrom the neural network and supervisory data may be calculated with aloss function. Next, based on the obtained errors, connection weightcoefficients and the like between nodes of the neural network areupdated by the backpropagation method so as to reduce the errors. Thebackpropagation method is a method for adjusting the connection weightcoefficients and the like between the nodes of each neural network so asto reduce the errors. Note that, in addition to the backpropagationmethod described above, a wide variety of well-known learning methodsincluding what one calls stacked autoencoder, dropout, noise addition,batch normalization, and sparsity regularization, and the like may beused in combination to improve processing accuracy.

When data is input into a learned model that has trained with suchtraining data, data conforming to a design of the learned model isoutput. For example, data probably corresponds to input data is outputaccording to tendency that is trained with the training data. In thepresent embodiment, when a tomographic image is input into a learnedmodel that has trained with the training data described above, anestimated distance to the optimal position of the coherence gate 64 isoutput.

Based on the estimated distance, the controlling unit 40 determines adriving amount of the driving motor 65 to drive the coherence gate 64next. The controlling unit 40 thus can function as an example of adetermining unit that determines, using the learned model, a drivingamount of the driving unit driving the optical path length differencechanging unit from a tomographic image of an eye to be examined. Notethat the driving amount determined by the determining unit correspondsto an actual driving amount of the driving unit and not necessarily thesame as an output from the learned model. The determining unit may usethe output from the learned model to determine the driving amount of thedriving unit; for example, a value resulting from converting theestimated distance output from the learned model into a number ofrevolutions or a number of steps of the driving unit may be used as thedriving amount. Alternatively, the determining unit may determine thedriving amount of the driving unit based on a value resulting fromadding a predetermined offset value or the like to the estimateddistance.

In a case where the learned model is a regression model, the controllingunit 40 can set an estimated distance output from the learned model as adriving amount of the driving motor 65 to drive the coherence gate 64next. In contrast, in a case where the learned model is a classificationmodel, data output from the learned model may be a single or a pluralityof estimated distance classes and a probability for the class. In thiscase, the controlling unit 40 may be configured so as to interpret anoutput result from the learned model as a probability for each estimateddistance class by using a softmax function in a final layer of themachine learning model.

In addition, the controlling unit 40 may adjust the estimated distanceoutput in step S43 according to the probability output from the learnedmodel. As an example, in a case where probabilities for two outputclasses are comparable to each other, a distance equivalent to a centerdistance of the classes may be determined to be an estimated distance tobe output. Note that a method for determining the estimated distanceoutput in step S43 from estimated distances of a plurality of classesoutput from the learned model is not limited to the above. For example,the controlling unit 40 may cause the display unit 70 to display theestimated distances of the plurality of classes output from the learnedmodel and the probabilities for the estimated distances, and an examinermay select an estimated distance or the driving amount of the drivingmotor 65 to be output in step S43 according to the display. In thiscase, the controlling unit 40 can determine the estimated distance orthe driving amount to be output according to instructions from anoperator.

In a learning method for reinforcement learning, the controlling unit 40obtains tomographic images while shifting the coherence gate 64 inrandom directions by random amounts and evaluates the tomographicimages. As an evaluation index, for example, a brightness of an imagecan be used.

In a case of imaging in the vitreous body mode, in which a DC componentof an interference signal appears in an upper part of the tomographicimage, an image corresponding to the interference signal is seenbrighter at an upper part of the tomographic image due tocharacteristics of coherence. Therefore, using a brightness of an imageas an evaluation index, adjustment processing can be performed so that aretina can be seen in a tomographic image. The evaluation index fortomographic images used here is not limited to the above, and anappropriate evaluation index can be set according to an adjustmentcriterion.

After evaluating the tomographic images, the controlling unit 40 movesthe coherence gate 64 at random again to obtain tomographic images andevaluates the newly obtained tomographic images. The controlling unit 40then calculates differences between evaluation values and uses thedifferences as rewards to train the neural network by thebackpropagation method so that a maximum reward is obtained. A targetfor the reinforcement learning may be set at, for example, reaching aposition that maximizes the reward in a shortest time.

A case where the tomographic image illustrated in the image (b) in FIG.6 is obtained before the coherence gate 64 is moved will be described asan example. In an example of the reinforcement learning, the controllingunit 40 first obtains an evaluation value Eb of the tomographic image atthis position. Next, assume that a tomographic image as illustrated inthe image (c) in FIG. 6 is obtained as a result of obtaining atomographic image by moving the coherence gate 64 at random. Thecontrolling unit 40 obtains an evaluation value Ec of the tomographicimage at this position. Using a difference Ec−Eb between the evaluationvalues as a reward, the controlling unit 40 performs the reinforcementlearning of the machine learning model.

By repeating such a learning operation, the machine learning model canlearn tomographic images and feature quantities for outputting estimateddistances to optimal positions of the coherence gate 64 corresponding tothe tomographic images. For such reinforcement learning, well-known whatone calls a Q-learning algorithm may be used, which will not describedin detail here. Note that, as an algorithm of the reinforcementlearning, for example, Saras, the Monte Carlo method, or a banditalgorithm may be used.

Also in the reinforcement learning, the learning may be performed byusing a model of an eye. Furthermore, in the reinforcement learning, thelearned model obtained in advance through learning with the model of aneye may be subjected to transfer learning in which additional learningis performed by using a human eye.

Note that the machine learning algorithm for the learned model is notlimited to the deep learning using the illustrated neural network. Themachine learning algorithm used for the processing may be anothermachine learning algorithm using, for example, a support vector machineor a Bayesian network.

In a case where the coherence gate 64 is driven based on the estimateddistance obtained by using the learned model as described above, thedriving amount may be offset in a direction in which a tomographic imageis shifted downward from a tomographic image obtained at the estimateddistance. An offset amount can be set at, for example, ⅕ of a range inthe Z direction of the imaging for the tomographic image. In thismanner, the entire retina can be easily made to be seen in thetomographic image without being turned down.

The above description is given of an example of using a learned model inwhich input data is tomographic images that are images obtained by usingcombined light of the measurement light and the reference light.However, the tomographic images input into the learned model are notlimited to the above and may be tomographic images generated byperforming any image processing and the like on images obtained by usingthe combined light. For example, tomographic images obtained byperforming correction processing on images obtained by using thecombined light may be used as the input data of the learned model. Thecorrection processing can be performed by the controlling unit 40, andthe controlling unit 40 can function as an example of a correcting unitthat performs the correction processing on an image. In this case,tomographic images subjected to image processing such as the correctionprocessing can be similarly used as input data of the training data.

An example of the correction processing may be processing for binarizinga tomographic image. The binarization processing is performed such thatan intensity value of a pixel having an intensity value equal to orhigher than a preset threshold value is set at one, and an intensityvalue of a pixel having an intensity value lower than the thresholdvalue is set at zero. As the threshold value, for example, a value thatis 0.3 times a maximum intensity value can be set. A method for thebinarization processing is not limited to the above, and various methodssuch as the “Otsu's method” can be used. After the binarizationprocessing, skeletonization processing may be performed.

An example of the correction processing may be processing for enlargingor reducing a tomographic image in a longitudinal direction based ondata on an eye axial length or a visual acuity that is separately inputinto the controlling unit 40. In a case of reducing a tomographic imagein the longitudinal direction, for example, reduction processing can beperformed while a Z coordinate of an uppermost pixel out of pixelshaving intensity values equal to or higher than a threshold value in thetomographic image is fixed. In an edge portion, a region of pixels thatare made to have no intensity value by the reduction may be embeddedwith pixels having an average intensity value of neighbor pixels.

Furthermore, an example of the correction processing may be processingfor extracting part of a tomographic image. An example of processing forextracting part of a tomographic image will be described with referenceto FIG. 10. An image (a) in FIG. 10 illustrates an example of atomographic image before the extraction. In the image (a) in FIG. 10, aninside between two dotted lines indicates a center portion of thetomographic image, and portions outside the two dotted lines indicateperipheral portions of the tomographic image.

The processing for extracting part of the tomographic image may be, forexample, processing for extracting only the center portion asillustrated in an image (b) in FIG. 10. Alternatively, the processingfor extracting part of the tomographic image may be processing forextracting only the peripheral portions as illustrated in an image (c)in FIG. 10. Alternatively, the processing for extracting part of thetomographic image may be processing for extracting only somerepresentative columns (A-scan data items) as illustrated in an image(d) in FIG. 10. Alternatively, an examiner may select a region to beextracted from the tomographic image while watching the display unit 70.By extracting a partial region in this manner, features of more interestcan be extracted, and a computational load can be reduced at the sametime.

Furthermore, an example of the correction processing may be processingusing a mirror-image part. This processing will be described withreference to FIG. 11. An image (a) in FIG. 11 illustrates an example ofa signal corresponding to a tomographic image that is obtained byperforming Fourier transform on an interference signal obtained by theOCT apparatus. Of the signal illustrated in the image (a) in FIG. 11, amirror-image part 902 mentioned here is a portion opposite side of a DCcomponent to a real-image part 901 that is displayed on the display unit70 as a tomographic image.

In an example of the processing using a mirror-image part, an imageincluding both the real-image part 901 and the mirror-image part 902 isused as input data of the learned model. Note that an example of theprocessing using a mirror-image part is not limited to the above.

For example, in another example of the processing using a mirror-imagepart, as illustrated in an image (b) in FIG. 11, an image in which onlyimage components forming a downward convex to be observed are extractedby removing image components forming an upward convex from thetomographic image is generated. Processing for generating the image inwhich only the image components forming the downward convex areextracted is an example of processing for generating an image in whichsome image components are extracted from the tomographic image includingboth the real-image part 901 and the mirror-image part 902. Thisprocessing may be performed using a machine learning model thatgenerates an image in which only the image components forming thedownward convex are seen, from an image in which the image componentsforming the upward convex and the image components forming the downwardconvex are seen. Alternatively, the image in which only the imagecomponents forming the downward convex are seen may be generated byusing segmentation processing to extract the image components formingthe upward convex and the image components forming the downward convexand to leave only the image components forming the downward convex.Alternatively, an image in which only image components forming adownward convex are seen may be generated by using other type ofprocessing such as rule-based one.

In a case where the coherence gate 64 deviates from the optimalposition, at least some of components forming a downward convex may beseen in the mirror-image part 902, as illustrated in an image (c) and animage (d) in FIG. 11. Therefore, the estimated distance of the coherencegate 64 may be obtained from a learned model using an input image beingan image that includes both regions of the real-image part 901 and themirror-image part 902 and in which only image components forming adownward convex is extracted. In this case, the image components formingthe downward convex seen in the mirror-image part 902 can be used forthe learning or inference, by which accuracy of the inference can beimproved.

Although an example of a case where the DC component of the real-imagepart 901 is on a vitreous body side, this is not limitative. In a casewhere the DC component of the real-image part 901 is on a choroid side,a position at which the mirror image is turned down with respect to thereal image is below the real image, but the same idea can be applied tothis case.

Furthermore, an example of the correction processing may include atleast one of smoothing processing and contrast adjustment processing.This enables improvement in quality of tomographic images and enablesimprovement in accuracy of inference processing.

As a learned model used for determining an estimated distance of thecoherence gate 64, the controlling unit 40 may include a plurality oflearned models that corresponds to a plurality of imaging conditions andconditions of eyes to be examined. In this case, the controlling unit 40selects a model to be used for the inference according to an imagingcondition and a condition of an eye to be examined. The controlling unit40 thus can function as an example of a selecting unit that selects alearned model corresponding to obtained tomographic images. In thiscase, the controlling unit 40 functioning as a determining unit candetermine the driving amount of the driving unit from an obtainedtomographic image using the selected learned model. The controlling unit40 can include learned models for each imaging view angle or depth rangeof imaging, which is an example of the imaging condition, for example.

In addition, in a case where a single program is used to perform imageprocessing for a plurality of models of OCT apparatuses, the controllingunit 40 can include learned models that corresponds to the models of theOCT apparatuses. This enables, for example, a single program to beapplied to both an SD-OCT apparatus and an SS-OCT apparatus.

The imaging conditions may include imaging modes such as a mode forobtaining a reverse image, which is suitable for observing a deepportion such as a choroid (choroid mode, or an enhanced depth imaging:EDI) and a mode for obtaining a normal image (vitreous body mode). Inthis case, the controlling unit 40 can include, for example, learnedmodels that corresponds to both the mode for obtaining a reverse imageand the mode for obtaining a normal image. There is a difference betweenthe vitreous body mode and the choroid mode in that the vitreous bodymode causes a retina to be seen in an upper part of a tomographic imagebrighter, while the choroid mode causes a retina to be seen in a lowerpart of a tomographic image darker, and thus a change in how a retina isseen when the coherence gate 64 is driven differs between the vitreousbody mode and the choroid mode. For example, when the coherence gate 64is driven from a position of the image (b) in FIG. 6 in a direction thatcauses the retina to move upward, the retina becomes brighter in animage in the vitreous body mode, while the retina becomes darker in animage in the choroid mode. In the vitreous body mode, when the coherencegate 64 is driven from the position of the image (b) in FIG. 6 in adirection that causes the tomographic image to move upward, an image inwhich the retina is seen bright as the image (d) in FIG. 6 is obtained.In contrast, in the choroid mode, when the coherence gate 64 is drivenfrom the position of the image (b) in FIG. 6 in a direction that causesthe tomographic image to move downward, an image in which the retina isseen bright as the image (d) in FIG. 6 is obtained. Since behavior thatappears when the coherence gate 64 is driven thus differs between thevitreous body mode and the choroid mode, these modes can be supported byseparately preparing learned models that correspond to the respectivemodes. At this time, as the learned model, any one of a plurality oflearned models that corresponds to a plurality of target positions forthe optical path length difference changing unit may be selected, theplurality of target positions each being a target for the adjustment ofthe optical path length difference.

In addition, the imaging conditions may include a mode for generating apanoramic image. In this case, the controlling unit 40 can include, as alearned model that corresponds to the mode for generating a panoramicimage, a learned model that is obtained through learning withtomographic images for which a position of fixation is changed by theinternal fixation lamp 110.

Examples of a condition of an eye to be examined include whether the eyeto be examined is myopia or high myopia or not. For example, thecontrolling unit 40 may include a learned model that has trained witheyes to be examined being myopia and a learned model that has trainedwith eyes to be examined not being myopia, and which of the learnedmodels is to be applied may be selected by the controlling unit 40 or anexaminer who has referred to patient data.

The controlling unit 40 may perform processing for converting anestimated distance obtained by using a learned model into a drivingamount of the driving motor 65 based on an imaging condition, acondition of an eye to be examined, an offset amount, or the like. Forexample, the controlling unit 40 may correct an obtained estimateddistance and convert the estimated distance into the driving amount ofthe driving motor 65 according to an imaging condition (the imaging viewangle, the depth range of imaging, the model, whether the mode is thevitreous body mode or the choroid mode) or a condition of an eye to beexamined (whether the eye is myopia or not). The controlling unit 40 mayconvert a value obtained by offsetting an obtained estimated distanceinto the driving amount of the driving motor 65 based on an optimalposition for a retina in a tomographic image that has been set by anexaminer. By configuring the controlling unit 40 to perform theconverting processing as described above, the number and a capacity oflearned models included in the controlling unit 40 can be reduced ascompared with a case where the controlling unit 40 includes a pluralityof learned models corresponding to the imaging conditions. Furthermore,this configuration reduces trouble of reloading a learned model wheneveran imaging condition is changed.

To this end, for example, as illustrated in an image (a) in FIG. 12, acorrect position of the coherence gate 64 may be determined to be aposition of the coherence gate 64 when a lowermost part of a retina in atomographic image is located at a standard height, and a common learningmodel may be generated for different imaging view angles. After anestimated distance is obtained with this learned model, offsettingprocessing may be performed such that the lowermost part of the retinabecomes located at an upper position with a narrower angle of view asillustrated in an image (b) in FIG. 12. The offset amount here can bedetermined as appropriate on a rule basis based on an imaging viewangle. The offsetting processing here may be processing for driving thecoherence gate 64 by a distance resulting from subtracting the offsetamount from the estimated distance or may be processing for driving thecoherence gate 64 by the estimated distance and then driving thecoherence gate 64 again by the offset amount. This offsetting processingmay be performed only when a condition for step S46 described later issatisfied. A method for setting a criterion for generating the commonlearning model is not limited to the above, and various methods can beused. That is, the controlling unit 40 as an example of the determiningunit may determine a driving amount of the driving motor 65 as anexample of the driving unit by adding an offset amount corresponding toan imaging view angle included in an imaging condition to an outputvalue to be described later (or a small value to be described later).For example, the offset amount corresponding to a narrow imaging viewangle may be set such that a retina region in a tomographic imageobtained by imaging with the narrow imaging view angle is shifted closerto a vitreous body region than a retina region in a tomographic imageobtained by imaging with a wide imaging view angle. At this time, theoffset amount corresponding to the wide imaging view angle may be zero.Alternatively, the offset amount corresponding to a wide imaging viewangle may be set such that a retina region in a tomographic imageobtained by imaging with the wide imaging view angle is shifted awayfrom the vitreous body region than a retina region in a tomographicimage obtained by imaging with a narrow imaging view angle. At thistime, the offset amount corresponding to the narrow imaging view anglemay be zero.

In addition, the controlling unit 40 may be configured to performtrimming processing for leaving a center portion in a case of a largeimaging view angle or depth range of imaging and not to perform thetrimming processing in a case of a small imaging view angle or depthrange of imaging. In this case, view angles or a depth ranges oftomographic images input into a learned model can be standardized. Atthis time, the controlling unit 40 may adjust an image size by thinningor interpolating an image as necessary. Performing such processingenables a common learned model to be used for different imaging viewangles or depth ranges of imaging. Therefore, the number and a capacityof learned models included in the controlling unit 40 can be reduced ascompared with a case where the controlling unit 40 includes a pluralityof learned models corresponding to the imaging conditions.

Evaluation of a performance of a created learned model may be conductedbased on a difference between an estimated distance and a ground truthlabel. In addition, the shifting processing described with reference toFIG. 8 may be used to move the coherence gate 64 by an estimateddistance to generate a pseudo adjusted image, and the pseudo adjustedimage may be evaluated qualitatively or on a rule basis.

In the above-described manner, in step S43, the controlling unit 40 usesa learned model to obtain an estimated distance of the coherence gate 64from a tomographic image. As the controlling unit 40 obtains theestimated distance, the processing proceeds to step S44.

In step S44, the controlling unit 40 increments the number i of trialsby one. As the controlling unit 40 increments the number i of trials,the processing proceeds to step S45.

In step S45, the controlling unit 40 determines whether the estimateddistance obtained in step S43 is equal to or lower than the targetdistance. The target distance is an index of a distance for achieving atarget adjustment accuracy and can be expressed by, for example, adeviation amount (distance) from an adjusted position as a target. Thetarget distance may be set at, for example, 300 μm. Alternatively, thetarget distance may be determined based on an imaging range of a retinaand the number of pixels of a tomographic image. In addition, the targetdistance may be set for each examiner and every imaging. The controllingunit 40 may have an imaging mode for each of a plurality of targetdistances, and the target distance may be set according to an imagingmode selected by an examiner. In a case where the estimated distance isdetermined to be equal to or lower than the target distance, theprocessing proceeds to step S46.

In step S46, the controlling unit 40 determines whether the estimateddistance obtained in step S43 is equal to or lower than the targetdistance for a predetermined consecutive number of times N. Thepredetermined consecutive number of times N here is an index thatindicates a stability for determining whether to finish the fineadjustment. The predetermined consecutive number of times N can be setat, for example, three. In a case where the estimated distance isdetermined to be “equal to or lower than the target distance for thepredetermined consecutive number of times N”, the controlling unit 40finishes the fine adjustment processing of coherence gate. Thepredetermined consecutive number of times N can be set at one, and whenthe condition is satisfied in step S46, the adjustment can be finishedby the determination performed only once. Note that the predeterminedconsecutive number of times N is not limited to three and one and may beset at any number according to a desired configuration.

In contrast, in a case where the estimated distance is determined not tobe “equal to or lower than the target distance for the predeterminedconsecutive number of times”, the processing proceeds to step S47. Instep S47, the controlling unit 40 drives the coherence gate 64 by theestimated distance. The processing then returns to step S42, where thecontrolling unit 40 performs the processing according to the flow asdescribed above again.

In step S46, whether the estimated distance obtained in step S43 isequal to or lower than the target distance for a certain number of timesout of the predetermined consecutive number of times may be used as anindex for the determination. For example, whether the estimated distanceis equal to or lower than the target distance for three times or greaterout of five consecutive times may be used as an index for thedetermination. Note that the predetermined consecutive number of timesand the certain number of times are not limited to the above and may beset at any number according to a desired configuration.

In a case where the estimated distance is determined not to be equal toor lower than the target distance in step S45, the processing proceedsto step S48. In step S48, the controlling unit 40 determines whether thenumber i of trials is lower than a preset maximum number of trial. Themaximum number of trial can be set at, for example, five. Note that themaximum number of trial may be set at any number according to a desiredconfiguration.

In a case where the number i of trials is determined to be lower thanthe maximum number of trial in step S48, the processing proceeds to stepS49. In step S49, the controlling unit 40 drives the coherence gate 64by the estimated distance. In a case where the estimated distance isgreater than a predetermined distance threshold value Lmax, the drivingamount of the driving in step S49 may be the distance threshold valueLmax. The distance threshold value Lmax can be set at, for example, thesame value as that of a depth range of a tomographic image.Alternatively, the distance threshold value Lmax may be set at a smallvalue such as ⅕ of the depth range of a tomographic image forlittle-by-little driving. However, the distance threshold value Lmax maybe set at any value according to a desired configuration. That is, thecontrolling unit 40 as an example of the determination unit maydetermine whether a value output from the learned model receiving theobtained tomographic image is greater than the threshold value. At thistime, in a case where the output value is equal to or lower than thethreshold value, the controlling unit 40 as an example of thedetermining unit may determine the output value as the driving amount.In addition, in a case where the output value is greater than thethreshold value, the controlling unit 40 may determine the thresholdvalue as the driving amount. Alternatively, the controlling unit 40 asan example of the determination unit may determine whether the drivingamount of the driving motor 65 as an example of the driving unit isgreater than a threshold value. At this time, in a case where thedriving amount is greater than the threshold value, the controlling unit40 as an example of the determining unit may determine the thresholdvalue as the driving amount.

The distance by which the controlling unit 40 drives the coherence gate64 in step S49 may be set according to the estimated distance. Forexample, the distance by which the coherence gate 64 is to be driven canbe set at half the estimated distance. This increases the number of theinference, so that converging adjustment can be performed more robustly.In this case, the maximum number of trial in step S48 can be set to begreater than 5 in the above-described case, for example, set at 15. Notethat the maximum number of trial may be set at any number according to adesired configuration. The distance by which the coherence gate 64 inthis case is not limited to the half the estimated distance and can beset optionally according to the estimated distance. That is, thecontrolling unit 40 as an example of the determining unit may determinethe driving amount of the driving motor 65 as an example of the drivingunit by converting the value output from the learned model receiving theobtained tomographic image into a value smaller than the output value.At this time, in a case where the output value is equal to or lower thanthe threshold value, the controlling unit 40 may determine the drivingamount by converting the output value into a value smaller than theoutput value. In addition, in a case where the output value is greaterthan the threshold value, the controlling unit 40 may determine a valuesmaller than the threshold value as the driving amount. Alternatively,the controlling unit 40 may converts the value output from the learnedmodel receiving the obtained tomographic image into a value smaller thanthe output value, and in a case where the smaller value is equal to orlower than the threshold value, the controlling unit 40 may determinethe smaller value as the driving amount. In addition, in a case wherethe smaller value is greater than the threshold value, the controllingunit 40 may determine the threshold value as the driving amount.

The processing then returns to step S42, where the controlling unit 40performs the processing according to the flow as described above again.

In contrast, in a case where the number i of trials is determined to beequal to or greater than the maximum number of trial in step S48, theprocessing proceeds to exception processing in step S50. An example ofthe exception processing in step S50 will be described below.

An example of the exception processing may be processing for driving thecoherence gate 64 to the rough-estimated position obtained in step S31.In addition, the processing may then cause the display unit 70 todisplay a message that prompts an examiner to perform a manual operationas necessary. In this case, the examiner can perform manual adjustmenton the position of the coherence gate 64 as necessary by operating acoherence gate adjuster 207 in the imaging screen 200 illustrated inFIG. 2.

An example of the exception processing may be processing for driving thecoherence gate 64 based on intensity values of the tomographic image.The example may be processing in which, for example, the coherence gate64 is driven to the rough-estimated position, then an intensity value ofa tomographic image within a reference range is obtained every time thecoherence gate 64 is driven by a certain distance, and the coherencegate 64 is driven to a position at which the value is maximized. Thereference range here can be set at, for example, a position in thetomographic image suitable for observation. In a case where there is nopeak of changes in the intensity value within the reference range, thecoherence gate 64 may be driven again in the same direction as that ofdriving to the rough-estimated position by the same distance.

In the exception processing, when driving the coherence gate 64 to therough-estimated position, the controlling unit 40 may sequentiallyobtain the estimated distance in the processing in step S43 to determinewhether the coherence gate 64 has moved closer to or away from theoptimal position. From a result of the determination, a direction inwhich the coherence gate 64 is to be driven by the certain distance maybe set at a direction in which the coherence gate 64 is moved close tothe correct position. The certain distance here may be set at any value;for example, the certain distance can be set at the same value as thatof the depth range of an imaged tomographic image.

Another example of the exception processing may be processing in whichthe coherence gate 64 is driven to an end of a range where the coherencegate 64 can be driven, then an intensity value of a tomographic imagewithin a reference range is obtained every time the coherence gate 64 isdriven by the certain distance, and the coherence gate 64 is driven to aposition at which the value is maximized. The certain distance here maybe set at any value; for example, the certain distance can be set at theentire range where the coherence gate 64 can be driven. In addition, thereference range here can be set at, for example, a position in thetomographic image suitable for observation.

An example of the exception processing may be processing for moving thecoherence gate 64 to a center position of the range where the coherencegate 64 can be driven. The center position of the range where thecoherence gate 64 can be driven here may be designed to be, for example,an optimal position for the coherence gate 64 for a fundus of an averagediopter scale.

The controlling unit 40 can perform any one of the types of exceptionprocessing described above in step S50. After the exception processingis performed in step S50, the controlling unit 40 finishes the fineadjustment processing of coherence gate.

In a case where the adjustment is determined to be difficult to perform,the processing may proceed to the exception processing in step S50before the number i of trials reaches the maximum number of trial instep S48. Examples of the case where the adjustment is difficult toperform include a case of a peculiar retina shape and a case where animage quality is poor under an apparatus condition. An example of how todetermine whether the adjustment is difficult will be described below.First, after step S43, an estimated target position for the coherencegate 64 is calculated by subtracting the estimated distance from acurrent position of the coherence gate 64. In a case where the estimatedtarget position fluctuates by a threshold value or greater for apredetermined number of times in a row, the controlling unit 40determines that the adjustment is difficult. That is, the controllingunit 40 may perform the exception processing in a case where the numberof times the fluctuation in the target position estimated by using thevalue output from the learned model receiving the obtained tomographicimage (estimated distance) and the current position of the coherencegate 64 is equal to or greater than the threshold value reaches thepredetermined number of times. At this time, the exception processingmay include processing for driving the coherence gate 64 to therough-estimated position described above. In addition, the exceptionprocessing may include processing for prompting an examiner to perform amanual operation. In addition, the exception processing may includeprocessing for driving the coherence gate 64 based on intensity valuesof the tomographic image. The exception processing may include at leastone of these types of processing. The threshold value here can be set,as an instability level, based on inference accuracy of the learnedmodel or an inter-frame spacing of the training data and may be set at avalue that is sufficiently large with respect to the inference accuracyor the inter-frame spacing. As an example, the threshold value can beset at a value that is three times the inference accuracy of the learnedmodel or the inter-frame spacing of the training data. In addition, thepredetermined number of times can be set to be smaller than the maximumnumber of trial; as an example, the predetermined number of times can beset to be about half the maximum number of trial. In a case where theadjustment is difficult, this enables the adjustment to be interruptedwithout waiting for the number of trials to reach the maximum number oftrial, and thus a time taken until the processing proceeds to theexception processing can be shortened.

The distance by which the controlling unit 40 drives the coherence gate64 in step S49 may be set according to an estimated distance that isobtained in the past. For example, the distance by which the coherencegate 64 is to be driven may be set to be equal to or lower than aprevious estimated distance. This can reduce a risk that the coherencegate 64 is driven to a position far away from the correct position dueto an erroneous estimation in a case where a blink specifically occursduring the adjustment.

In addition, in step S45, from an estimated distance or an estimatedtarget position obtained in the past, the next estimated distance may bepredicted. The next estimated distance can be predicted by regressionanalysis or the like on the estimated distance or the estimated targetposition obtained in the past. In a case where the estimated distancenext obtained is significantly different from the prediction by athreshold value or greater, the obtained estimated distance may bedetermined to be an abnormal value. The threshold value here can be set,as an instability level, based on inference accuracy of the learnedmodel or an inter-frame spacing of the training data and may be set at avalue that is sufficiently large with respect to the inference accuracyor the inter-frame spacing. As an example, a value that is three timesthe inference accuracy of the learned model or the inter-frame spacingof the training data can be set as the threshold value. In a case wherethe obtained estimated distance is determined to be an abnormalityvalue, the driving of the coherence gate 64 based on this estimateddistance may not be performed, and the estimated distance may beobtained again in the next frame. At this time, the number i of trialsmay be incremented or may not be incremented. In a case where theestimated distance is determined to be an abnormality value for apredetermined number of times, the coherence gate 64 may be driven basedon the last estimated distance. In the case where the estimated distanceis determined to be an abnormality value for a predetermined number oftimes, the exception processing may be performed. The exceptionprocessing here may include processing for driving the coherence gate 64to the rough-estimated position and processing for adjusting thecoherence gate 64 based on a manual operation by an examiner orintensity values of the tomographic image. Alternatively, the coherencegate 64 may be driven based on the estimated distance predicted to benext obtained.

When an examiner determines that a manual adjustment is needed after thefine adjustment processing of coherence gate is finished, the manualadjustment may be performed by operating the coherence gate adjuster 207in the imaging screen 200 illustrated in FIG. 2. In addition, after thefine adjustment processing of coherence gate is finished, thecontrolling unit 40 may store a current tomographic image as an optimalimage. In a case where the adjustment of the coherence gate 64 is to beperformed again, the driving amount of the driving motor 65 may bedetermined based on a difference between an estimated distance obtainedfrom a tomographic image at the time and an estimated distance obtainedfrom the optimal image.

Through the processing described above, the controlling unit 40 finishesthe position adjustment processing of the coherence gate. After thecoherence gate 64 is adjusted in the above-described manner, focusadjustment may be performed on the OCT as necessary. In this manner, theOCT apparatus can obtain the tomographic image 206.

As described above, an OCT apparatus according to the present embodimentthat obtains a tomographic image of an eye to be examined by usingcombined light obtained by combining (a) return light from the eye to beexamined irradiated with measurement light and (b) reference lightincludes an optical path length difference changing unit, a drivingunit, a determining unit, and a controlling unit. The optical pathlength difference changing unit changes an optical path lengthdifference between the measurement light and the reference light, andthe driving unit drives the optical path length difference changingunit. The determining unit determines, using a learned model, a drivingamount of the driving unit from the obtained tomographic image, and thecontrolling unit controls the driving unit using the determined drivingamount. Here, the coherence gate 64 functions as an example of theoptical path length difference changing unit, the controlling unit 40functions as an example of the controlling unit and the determiningunit, and the driving motor 65 function as an example of the drivingunit.

More specifically, the determining unit uses the learned model to obtainan estimated distance from a current position to an optimal position ofthe optical path length difference changing unit from the obtainedtomographic image, and determines the driving amount based on theestimated distance. In particular, the determining unit according to thepresent embodiment determines an estimated distance that is output fromthe learned model receiving the obtained tomographic image as thedriving amount of the driving unit. The OCT apparatus according to thepresent embodiment can perform alignment using the optical path lengthdifference changing unit in this manner, and the learned model caninclude a learned model for the alignment. The learned model may be aregression model or may be a classification model. In a case where thelearned model is a regression model, the learned model can be obtainedby supervised learning using a plurality of training data items thatincludes a plurality of tomographic images and continuous valuesobtained from different optical path length differences, each of whichis an optical path length difference between the measurement light andthe reference light. Here, the continuous values may be each anestimated distance to a target position of the optical path lengthdifference changing unit, a driving amount of the driving unit fordriving the optical path length difference changing unit to the targetposition, or the like.

With such a configuration, the OCT apparatus according to the presentembodiment can drive the coherence gate 64 toward the optimal positionbased on the estimated distance obtained by using the learned model.Thus, the OCT apparatus can adjust the optical path length differencebetween the measurement light and the reference light with highaccuracy.

In addition, the OCT apparatus can further include a selecting unit thatselects a learned model that corresponds to the obtained tomographicimage, the learned model being any one of a plurality of learned modelsthat corresponds to a plurality of imaging conditions. Here, thecontrolling unit 40 can function as an example of the selecting unit.The determining unit may determine the driving amount of the drivingunit from the obtained tomographic image using the selected learnedmodel. Here, the obtained tomographic image may be a tomographic imagethat is obtained by performing correction processing on an imageobtained by using the combined light. The correction processing may beone of processing for binarizing the image, processing for extractingsome region of the image, processing for generating a tomographic imageincluding both a real-image part and a mirror-image part, and processingfor generating an image in which some image components are extractedfrom the tomographic image including both the real-image part and themirror-image part.

The controlling unit may determine whether to drive the optical pathlength difference changing unit according to how long the estimateddistance is. In this case, the controlling unit can perform exceptionprocessing in a case where the estimated distance is longer than thetarget distance. The exception processing can include processing foradjusting the optical path length difference changing unit based on amanual operation by an examiner or intensity values of the tomographicimage.

Note that, in the present embodiment, the output of the learned model istaken as the estimated distance, and the estimated distance output fromthe learned model is set as the driving amount of the driving motor 65to drive the coherence gate 64 next. In contrast, the output of thelearned model may be the driving amount of the driving unit such as anumber of revolutions of the driving motor 65 for driving the coherencegate 64. Here, the driving amount of the driving unit such as the numberof revolutions of the driving motor 65 differs between apparatuses.Thus, for training data relating to the learned model, the drivingamount of the driving unit obtained by using an OCT apparatus or adriving unit that is the same as or of the same type of an opticalsystem of an OCT apparatus or a driving unit to be operated is to beused. In addition, in a case where the output of the learned model is tobe used as the driving amount of the driving unit, a driving amountresulting from adding or subtracting a predetermined offset amount to orfrom the driving amount of the driving unit for driving the coherencegate 64 to the optimal position may be used as the training data. Inthis case, the entire retina can be easily made to be seen in thetomographic image without being turned down.

The output of the learned model may be a distance on the image or thenumber of pixels from a current position to a target position of apartial region of the tomographic image (retina region, etc.). In thiscase, for the training data, the distance or the number of pixels fromthe current position to the target position of the partial region of thetomographic image may be used. Note that, in this case, the controllingunit 40 needs to determine the driving amount of the driving unit fromthe distance or the number of pixels output from the learned model, forexample, the number of revolutions of the driving motor 65. For example,the controlling unit 40 can determine the driving amount of the drivingmotor 65 by converting the distance on the image or the number of pixelsoutput from the learned model into the number of revolutions of thedriving motor 65 using a preset conversion table stored in a storageunit not illustrated.

Note that although a method for adjusting the coherence gate 64 toobtain a tomographic image of a fundus is described in the presentembodiment, application of this adjusting method is not limited tofundi. For example, the adjusting method described above may be used toobtain a tomographic image of an anterior ocular segment.

Embodiment 2

Next, with reference to FIG. 13, Embodiment 2 of the present disclosurewill be described. In the present embodiment, driving of the coherencegate 64 is controlled by using a learned model to track a retina in atomographic image in a depth direction (Z direction) during imaging. Thetracking here refers to correction in the Z direction such that theretina is seen appropriately in a case where the retina is not seenappropriately in the tomographic image because a position of a facedeviates. Note that components of an OCT apparatus according to thepresent embodiment are the same as those of the OCT apparatus accordingto Embodiment 1 and thus will be denoted by the same reference numeralsand will not be described. Processing by a controlling unit 40 accordingto the present embodiment will be described below mainly aboutdifference from Embodiment 1.

FIG. 13 is a flowchart of tracking processing according to the presentembodiment. The processing is performed by the controlling unit 40. Asthe controlling unit 40 receives a start command, the processing isstarted. Examples of a timing for receiving the start command include atiming immediately after the adjustment of the coherence gate inEmbodiment 1 is finished and a simultaneous timing with start of imagingfor an OCT image or an OCTA image. Alternatively, the imaging screen 200may be provided with a button for specifying ON/OFF of the tracking, andthe start command for the tracking processing may be received as ON isspecified by an examiner.

First, in step S101, the controlling unit 40 obtains tomographic images.The tomographic images may be tomographic images sequentially obtainedin imaging. The tomographic images obtained by imaging are data having adensity higher than a density of preview images that are obtained foralignment, and thus the tomographic images obtained here may be obtainedby thinning the tomographic images obtained by imaging. By sequentiallyobtaining tomographic images in imaging and performing the followingadjustment processing, an OCT image for which a coherence gate 64 isadjusted to an optimal position at each of positions in an imaging viewangle. This enables a tomographic image that is satisfactory even at aperipheral portion in the imaging view angle to be obtained even in acase where a depth range of imaging of the apparatus is small or a casewhere a bend of the retina is large. In addition, a process of waiting acertain period of time may be provided before obtaining the tomographicimages in step S101. The certain period of time may be set according totarget specifications about a frame rate of the tracking.

The tomographic images obtained in step S101 may include a tomographicimage obtained at a representative XY position that is set in advance,out of the tomographic images sequentially obtained in imaging. Anexample of the representative XY position is a position on a center lineof the imaging view angle. Another example of the representative XYposition is an XY position at which a C-scan is started. By obtaining atomographic image only at the representative XY position and performingthe following adjustment processing, an OCT image for which a positionof the coherence gate 64 is kept constant in the imaging view angle canbe generated.

The controlling unit 40 determines an image quality of the tomographicimage obtained in step S101, and when the image quality is lower than athreshold value, the controlling unit 40 may not use the tomographicimage but may obtain a tomographic image of the next frame. This canprevent a malfunction caused by a blink of a subject that specificallydarkens a tomographic image. As an evaluation index for the imagequality, for example, an intensity value of the tomographic image in agiven range can be used.

Next, in step S102, the controlling unit 40 uses a learned model toobtain an estimated distance of the coherence gate 64 from the obtainedtomographic image. This processing in step S102 of this flowchart may bethe same processing as step S43 in Embodiment 1. The controlling unit 40may determine, as the estimated distance obtained in step S102, adifference between an estimated distance obtained with the learned modelfrom the tomographic image and an estimated distance obtained from theoptimal image stored after the fine adjustment processing of coherencegate in Embodiment 1 is finished. The controlling unit 40 may correctthe estimated distance based on the XY position of the tomographic imageobtained in step S101.

In step S103, the controlling unit 40 determines whether the estimateddistance obtained in step S102 is equal to or lower than a targetdistance. The target distance is an index of a distance for achieving atarget adjustment accuracy and can be indicated by, for example, adeviation amount (distance) from an adjusted position as a target. Thetarget distance may be set at, for example, 200 μm. Alternatively, thetarget distance may be determined based on an imaging range of a retinaand the number of pixels of a tomographic image.

In a case where the estimated distance is determined not to be equal toor lower than the target distance, the processing proceeds to step S104.In step S104, the controlling unit 40 drives the coherence gate 64 bythe estimated distance. The processing then returns to step S101, wherethe controlling unit 40 performs the processing according to the flowagain.

In contrast, in a case where the estimated distance is determined to beequal to or lower than the target distance in step S103, the processingproceeds to step S105. In step S105, the controlling unit 40 determineswhether an end command has been received. For example, the end commandcan be issued at the same time as the imaging is ended or when theimaging is interrupted. In a case where the end command has beenreceived, the controlling unit 40 ends the tracking processing. Incontrast, in a case where the end command has not been received, thecontrolling unit 40 returns the processing to step S101 and performs theprocessing according to the flow as described above.

The OCT apparatus according to the present embodiment performs thetracking using an optical path length difference changing unit in theabove-described manner, and the learned model can include a learnedmodel for the tracking. By obtaining an estimated distance of thecoherence gate 64 from a tomographic image using a learned model as inthe present embodiment, the tracking processing can be performed onvarious subjects with high accuracy.

Note that Embodiment 1 and Embodiment 2 may be combined together. Inthis case, the OCT apparatus can perform the alignment and the trackingwith the optical path length difference changing unit. In addition, thelearned model can separately include the learned model for the alignmentand the learned model for the tracking.

Modifications according to at least one of Embodiment 1 and Embodiment 2of the present disclosure will be described below.

Modification 1

In Modification 1 according to Embodiment 1 and Embodiment 2 of thepresent disclosure, the controlling unit 40 obtains an estimateddistance from tomographic images obtained at a plurality of XY positionsin step S43 or step S102. An example of obtaining the plurality oftomographic images will be described with reference to FIG. 14. An image(a) in FIG. 14 illustrates an example of the SLO image 203, where dottedlines L1, L2, L3 and L4 illustrate examples of the XY positions at whichthe tomographic images are obtained. The dotted lines L1 and L2 indicatecenter lines of the imaging view angle, and the dotted lines L3 and L4indicate an example of positions of a peripheral view angle. Images (b),(c), (d) and (e) in FIG. 14 illustrate an example of tomographic imagesobtained at positions indicated by the dotted lines L1, L2, L3 and L4.

In an example of how to obtain an estimated distance from the pluralityof tomographic images, the controlling unit 40 obtains estimateddistances from the tomographic images illustrated in the images (b) to(e) in FIG. 14 using a learned model. At this time, when a DC componentis on a vitreous body side, the controlling unit 40 determines a valuethat is estimated to be closest to a choroid out of the estimateddistances obtained from the tomographic images as a final estimateddistance. In contrast, when the DC component is on a choroid side, thecontrolling unit 40 determines a value that is estimated to be closestto a vitreous body out of the estimated distances obtained from thetomographic images as the final estimated distance. In this manner,tomographic images each having an image that is not turned up or downcan be obtained at the positions indicated by the dotted lines L1, L2,L3 and L4 in imaging.

In another example of how to obtain an estimated distance from theplurality of tomographic images, the controlling unit 40 obtainsestimated distances from the tomographic images illustrated in theimages (b) to (e) in FIG. 14 using a learned model and determines anaverage value of the estimated distances as the final estimateddistance. Alternatively, a value resulting from performing weightedaveraging on the estimated distances obtained from the tomographicimages illustrated in the images (b) to (e) in FIG. 14 may be determinedas the final estimated distance. In this manner, tomographic images eachhaving an image that is unlikely to be turned up or down can be obtainedat the positions indicated by the dotted lines L1, L2, L3 and L4 inimaging.

As described above, a determining unit according to the presentmodification determines a driving amount of a driving unit using aplurality of values output from a learned model receiving a plurality oftomographic images obtained at different positions of an eye to beexamined. With such a configuration, tomographic images each having animage that is unlikely to be turned up or down can be obtained atdifferent positions of an eye to be examined in imaging.

XY positions for obtaining tomographic images are not limited to thepositions indicated by dotted lines L1, L2, L3 and L4 illustrated in theimage (a) in FIG. 14 and can be set at any positions. For example, theXY positions for obtaining tomographic images may be only the positionsindicated by the dotted lines L1 and L2 or may be set at otherpositions.

Note that the pattern for scanning illustrated in the image (a) in FIG.14 to perform the fine adjustment on the coherence gate 64 in step S33is merely an example and does not limit patterns for scanning. Dependingon a scanning pattern in imaging, the controlling unit 40 may change asize and a shape of a scanning pattern and the number of samples forperforming the fine adjustment on the coherence gate 64, as appropriate.In the machine learning, one learned model may be provided so that theestimated distance can be calculated for various scanning patterns, ordifferent learned models may be provided for scanning patterns ofdifferent sizes for example. A learned model may be provided for atomographic image obtained at each of the positions, or a single modelmay be applied commonly to the tomographic images obtained at thepositions.

One combined image resulting from joining tomographic images obtained attwo or more XY positions together may be used as input data for amachine learning model. An example of how to generate the combined imageis arranging the tomographic images laterally or longitudinally. Anotherexample of how to generate the combined image is adding or averagingintensity values of each tomographic image to generate one image.

Alternatively, tomographic images obtained at two or more XY positionsmay be input, and feature quantities obtained from the tomographicimages in the middle of learning may be combined. For example, thefeature quantities of the tomographic images can be combined in orimmediately before a fully connected layer. How to combine the featurequantities obtained from the tomographic images in the middle of thelearning is not limited to the above, and the feature quantities can becombined by various methods.

Modification 2

In Modification 2 according to Embodiment 1 and Embodiment 2 of thepresent disclosure, the controlling unit 40 obtains an estimateddistance from tomographic images obtained at a plurality of Z positionsin step S43 or step S102. In the present modification, the controllingunit 40 obtains tomographic images at the plurality of Z positions whenthe coherence gate 64 is driven in step S32, step S47, step S49, or thelike.

As an example, a case where an estimated distance is obtained fromtomographic images obtained at two Z positions will be described withreference to FIG. 15. Images (a) and (b) in FIG. 15 illustrate anexample of tomographic images that are obtained while the coherence gate64 is driven. The image (b) in FIG. 15 is a tomographic image obtainedwhen the driving of the coherence gate 64 is finished, and the image (a)in FIG. 15 is a tomographic image obtained in a frame previous to aframe for obtaining the image (b). Note that an interval of the framesfor obtaining the tomographic images can be set optionally. For example,by adjusting a driving speed of the coherence gate 64 and a samplingtime, an inter-frame spacing can be set such that the coherence gate 64is positioned at about 500 μm intervals.

Using a learned model, the controlling unit 40 obtains estimateddistances lc1 and lc2 from the tomographic images illustrated in theimages (a) and (b) in FIG. 15, respectively. The controlling unit 40checks a direction D1 toward an optimal position of the coherence gate64 based on a sign of the estimated distance lc2. In addition, thecontrolling unit 40 determines whether the coherence gate 64 has movedcloser to or away from the optimal position by the driving of thecoherence gate 64 as described above, based on a sign of Δlc=lc2−lc1.Based on a result of this determination, the controlling unit 40determines a direction D2 in which the coherence gate 64 is to be drivennext so that the coherence gate 64 is moved close to the optimalposition.

When the direction D1 and the direction D2 match, the controlling unit40 then drives the coherence gate 64 in the direction D1 by theestimated distance lc2. In contrast, when D1 and D2 do not match, thecontrolling unit 40 then drives the coherence gate 64 in the directionD2 by a predetermined distance and obtains an estimated distance fromtomographic images again. The predetermined distance here can be set at,for example, a depth range of the tomographic images.

Although an example of obtaining an estimated distance from tomographicimages obtained at two Z positions is described in the presentmodification, the number of the Z positions is not limited to two, andthe same processing may be performed at a plurality of Z positions aslarger as three or more. Here, FIG. 16 illustrates an example of resultsof obtaining estimated distances at three or more Z positions of thecoherence gate 64. In FIG. 16, a line graph of solid lines indicatesestimated distances obtained actually, and a dotted line is anapproximate straight line of the line graph. In this case, based onwhether a slope of the approximate straight line is positive ornegative, the controlling unit 40 can determine whether the coherencegate 64 has moved closer to or away from the optimal position by beingdriven. Based on a result of this determination, the controlling unit 40can determine the direction D2 in which the coherence gate 64 is to bedriven next so that the coherence gate 64 is moved close to the optimalposition and perform the same processing as the above.

In the present modification, the approximate straight line illustratedin FIG. 16 may be created in the middle of the adjustment, and anestimated distance to be obtained next may be predicted from theapproximate straight line. In a case where the estimated distance nextobtained is significantly different from the prediction by a thresholdvalue or greater, the obtained estimated distance may be determined tobe an abnormal value. The threshold value here can be set, as aninstability level, based on inference accuracy of the learned model oran inter-frame spacing of the training data and may be set at a valuethat is sufficiently large with respect to the inference accuracy or theinter-frame spacing. As an example, the threshold value can be set at avalue that is three times the inference accuracy of the learned model orthe inter-frame spacing of the training data. In a case where theobtained estimated distance is determined to be an abnormality value,the driving of the coherence gate 64 based on this estimated distancemay not be performed, and the estimated distance may be obtained againin the next frame. In a case where the estimated distance is determinedto be an abnormality value for a predetermined number of times, thecoherence gate 64 may be driven based on the last estimated distance. Inthe case where the estimated distance is determined to be an abnormalityvalue for a predetermined number of times, the exception processing maybe performed. The exception processing may include processing fordriving the coherence gate 64 to a rough-estimated position andprocessing for adjusting the coherence gate 64 based on a manualoperation by an examiner or intensity values of the tomographic image.Alternatively, the coherence gate 64 may be driven based on theestimated distance predicted to be next obtained.

As described above, a determining unit according to the presentmodification determines a driving amount of a driving unit using aplurality of values output from a learned model receiving a plurality oftomographic images obtained for different optical path lengthdifferences. With such a configuration, a risk that the coherence gate64 is driven in an opposite direction to a correct direction due to anerror in the estimation or a risk that the coherence gate 64 is drivento a position different from a correct position due to erroneousestimation can be reduced.

Modification 3

In Modification 3 according to Embodiment 1 and Embodiment 2 of thepresent disclosure, the controlling unit 40 obtains an estimateddistance of the coherence gate 64 using a plurality of learned models instep S43 or step S102. The plurality of learned models can be generatedby combining various kinds of input data, machine learning algorithms,and output data.

Examples of the input data for the learned models that can be includedin the controlling unit 40 include tomographic images, binarized imagesof the tomographic images, and images resulting from extracting parts ofthe tomographic images. Algorithms for the machine learning models thatcan be included in the controlling unit 40 include, as described above,algorithms using a neural network or a decision tree and algorithms forvarious methods such as a support vector machine. Examples of the outputdata for the machine learning models that can be included in thecontrolling unit 40 include estimated distances of the coherence gate64, classification classes of the estimated distances, and directionsfrom current positions to optimal positions of the coherence gate 64. Bycombining the above, a plurality of machine learning models can beprovided.

Based on output results from such a plurality of machine learningmodels, the controlling unit 40 can determine a final estimateddistance. For example, the controlling unit 40 can determine the finalestimated distance by averaging estimated distances obtained from thedifferent machine learning models.

Modification 4

In Embodiment 1 and Embodiment 2, the driving amount of the drivingmotor 65 is determined by inputting tomographic images obtained by theOCT apparatus into a learned model. However, images input into thelearned model is not limited to tomographic images. In Modification 4according to Embodiment 1 and Embodiment 2 of the present disclosure, adriving amount of a driving motor 65 is determined by inputting fundusobservation images into a learned model. Examples of the fundusobservation images include fundus photographs and SLO images obtainedwith visible light or infrared light. The OCT apparatus described inEmbodiments 1 and 2 can obtain SLO images by using the SLO opticalsystem 80. The OCT apparatus may include, for example, a fundus camerafor imaging a fundus photograph.

For the present modification, the fact that deviation of an eye (retina)from an optimum Z position appears in a form of defocus in a fundusobservation image is utilized. Alternatively, the fact that brightnessof an SLO image changes as a result of defocus may be utilized for thepresent modification. For example, assume that a position of a coherencegate 64 is moved in coordination with a position of a focus lens 58. Inthis case, in a case where defocus occurs in a fundus observation imagecorresponding to the position of the focus lens 58, a distance by whichthe coherence gate 64 is to be moved can be estimated according to anamount of the defocus. Thus, a learned model according to the presentmodification is generated by learning combinations of fundus observationimages obtained in a focused state and a defocused state and deviationsof the coherence gate 64 for the fundus observation images from anoptimal position. Note that an apparatus configuration of the apparatusfor obtaining the fundus observation images used for the learning can bethe same or the same type of an apparatus configuration for obtainingfundus observation image in an OCT apparatus to be operated.

Combined images of tomographic images and fundus observation images maybe input into the learned model. The learned model here is generated bylearning combinations of the combined images of tomographic images andfundus observation images and deviations of the coherence gate 64 forthe combined images from the optimal position. A combined image as anexample here can be generated by arranging a tomographic image and afundus observation image to generate one image. Note that, in this case,the position of the coherence gate 64 may not be moved in coordinationwith the position of the focus lens 58.

Alternatively, the driving amount of the driving motor 65 may be finallydetermined by combining an estimated distance obtained by inputting atomographic image into the learned model and estimated distance obtainedby inputting a fundus observation image into the learned model. Examplesof how to combine the estimated distance obtained from a tomographicimage and the estimated distance obtained from a fundus observationimage include averaging these estimated distances. Note that, also inthis case, the position of the coherence gate 64 may not be moved incoordination with the position of the focus lens 58.

Alternatively, the tomographic image and the fundus observation imagemay be input, and feature quantities obtained from the images in themiddle of learning may be combined. For example, the feature quantitiesof the images can be combined in or immediately before a fully connectedlayer. How to combine the feature quantities obtained from the images inthe middle of the learning is not limited to the above, and the featurequantities can be combined by various methods.

Modification 5

In Modification 5 according to Embodiment 1 of the present disclosure,immediately before the fine adjustment is performed on a coherence gate64 in step S33, adjustment is performed such that at least part of aretina is seen in a tomographic image. In the present modification,during driving the coherence gate 64 in step S32, a controlling unit 40obtains tomographic images at predetermined time intervals and obtainsintensity values of the tomographic images within a reference range.

The controlling unit 40 obtains a tomographic image immediately beforeperforming the fine adjustment on the coherence gate 64 in step S33 anddetermines whether an intensity condition is satisfied. At this point,the controlling unit 40 can analyze the obtained tomographic image byany well-known method to obtain a frequency distribution of thetomographic image. Here, the intensity condition is for determiningwhether at least part of the retina to be observed is seen in a currenttomographic image. As an example of the intensity condition, whether thetop 5% of intensity values in the frequency distribution of thetomographic image are equal to or greater than a threshold value can beset. As another example of the intensity condition, whether at least oneof indices such as a variance, a standard deviation, a skewness, andkurtosis of the frequency distribution of the tomographic image is equalto or greater than a threshold value can be set. These threshold valuescan be set based on intensity values of a retina portion in atomographic image imaged after appropriate adjustment, a variance, astandard deviation, a skewness, a kurtosis of a frequency distributionof the tomographic image, or the like.

The controlling unit 40 may use a learned model to determine whether atomographic image satisfies the intensity condition. This machinelearning model can be generated by learning a large number of pairs oftomographic images and results of checking whether the intensitycondition is satisfied.

Alternatively, the controlling unit 40 may obtain a tomographic image aplurality of times at predetermined frame intervals and may determinewhether the intensity condition is satisfied for a tomographic imagehaving a highest intensity value out of the tomographic images. In thismanner, a malfunction caused by a blink of a subject that darkens atomographic image can be prevented. As an example, the controlling unit40 can obtain a tomographic image three times at an interval of 100milliseconds.

In a case where the intensity condition is not satisfied, thecontrolling unit 40 can perform correction processing of a coherencegate. With reference to FIG. 17, the correction processing of acoherence gate according to the present modification will be described.

First, in step S141, the controlling unit 40 refers to a change inintensity value obtained during the driving of the coherence gate instep S32 and determines whether there is a peak being equal to orgreater than a threshold value. The threshold value here can be set at avalue that can be considered to be sufficiently large with respect to anoise level.

In a case where it is determined that there is a peak being equal to orgreater than the threshold value, the processing proceeds to step S142.In step S142, the controlling unit 40 drives the coherence gate 64 in anopposite direction to the direction of driving the coherence gate instep S32. As an example, the controlling unit 40 can drive coherencegate 64 to a position of the coherence gate 64 at which the intensityvalue reaches the peak. After the driving of the coherence gate 64 instep S142 is finished, the controlling unit 40 finishes the correctionprocessing of the coherence gate according to the present modificationand causes the processing to proceed to step S33.

In contrast, in a case where it is determined that there is no peakbeing equal to or greater than the threshold value, the processingproceeds to step S143. In step S143, the controlling unit 40 drives thecoherence gate 64 in the same direction as the direction of driving thecoherence gate in step S32. The driving amount of the driving motor 65here may be set at any value; for example, the driving amount of thedriving motor 65 can be set at the same value as the depth range of animaged tomographic image. After the driving of the coherence gate 64 instep S143 is finished, the controlling unit 40 finishes the correctionprocessing of the coherence gate according to the present modificationand causes the processing to proceed to step S33.

As described above, by causing the processing to proceed to step S33 ina state where at least part of a retina is seen in a tomographic image,the fine adjustment can be performed on the coherence gate 64 morestably.

Modification 6

In Modification 6 according to Embodiment 1 of the present disclosure,immediately before whether the estimated distance is equal to or lowerthan the target distance is determined in step S45, whether a retina isseen in a tomographic image is determined based on the estimateddistance. A blink or the like is thereby detected.

In the present modification, tomographic images in which no retina isseen are included in training data. The tomographic images in which noretina is seen are given a ground truth label of the same estimateddistance or ground truth labels of estimated distances within a commonlimitative range. The tomographic images in which no retina is seen maybe obtained by performing imaging under a condition that causes an eyeto be examined not to be seen in the tomographic images. At this time,the tomographic images may be given a common ground truth label.

FIG. 18 illustrates an image of a wide angle of view (the image (a) inFIG. 18) and an image of a narrow angle of view (the image (b) in FIG.18) obtained when the coherence gate 64 is set at some position. Theimage (b) in FIG. 18 is an image imaged with an angle of viewillustrated as an inside between dotted lines in the image (a) in FIG.18. As described above, a tomographic image may be obtained at aposition of the coherence gate 64 that causes the retina to be seen whenimaged with a wide angle of view (the image (a) in FIG. 18) and causesthe retina not to be seen when imaged with a narrow angle of view (theimage (b) in FIG. 18). In this case, tomographic images in which noretina is seen are given labels of estimated distances within a certainrange.

In the present modification, the controlling unit 40 subtracts theestimated distance from a current position of the coherence gate 64 tocalculate an estimated target position for the coherence gate 64. Thecontrolling unit 40 then compares a previous estimated target positionand the current estimated target position. In a case where a differencebetween the previous estimated target position and the current estimatedtarget position is equal to or greater than a threshold value, and thecurrent estimated distance is within the range of labels that are givenbecause no retina is seen as described above, the current tomographicimage is determined to be with no retina seen. Here, the threshold valuefor the difference between the previous estimated target position andthe current estimated target position can be set, as an instabilitylevel, based on inference accuracy of the learned model or aninter-frame spacing of the training data and may be set at a value thatis sufficiently large with respect to the inference accuracy or theinter-frame spacing. As an example, the threshold value can be set at avalue that is three times the inference accuracy of the learned model orthe inter-frame spacing of the training data. However, the value of thethreshold value is not limited to the above and can be set at any value.Alternatively, whether a retina is seen in a tomographic image may bedetermined based on, in place of the comparison between the previousestimated target position and the current estimated target position,whether a difference value between the previous estimated distance andthe current estimated distance is sufficiently large with respect to arecent driving amount of the coherence gate 64.

In a case where the current tomographic image is determined to be withno retina seen, the coherence gate 64 is not driven, and the processingreturns to step S42. At this time, a certain period of waiting time maybe provided before the processing returns to step S42. This waiting timecan be set at, for example, 100 ms, and the provision of this waitingtime increases a possibility that a blink has already ended at the nextestimation. The waiting time here can be set at any value.

The determination as to whether a retina is seen in the tomographicimage may be performed immediately before step S44, and in this case,the number i of trials is not incremented.

Modification 7

In Modification 7 according to Embodiment 1 of the present disclosure, acase where a coherence gate 64 has been driven in an opposite directionto a direction toward a correct position is detected based on anestimated distance immediately after step S43 or immediately after stepS44. In particular, since there is a case where images obtained atpositions of the coherence gate 64 away from each other have similarimage structures as with the images (a) and (e) in FIG. 6, there isconcern about occurrence of erroneous estimation in step S43 that maycause the coherence gate 64 to be driven in the opposite direction tothe direction toward the correct position. In addition, driving thecoherence gate 64 in the opposite direction to the direction toward thecorrect position results in a decrease in coherence due to being awayfrom a DC component, which can cause a retina not to be seen in animage. In a case where images in which no retina is seen are learnedwith some ground truth labels as in Modification 6, a certain estimateddistance is output while no retina is seen in images. In a case wherethis estimated distance is in the opposite direction to the directiontoward the correct position, the coherence gate 64 continues to move inthe opposite direction from the correct position. As countermeasuresagainst these problems, the detection described in the presentmodification is effective.

First, an estimated target position for the coherence gate 64 iscalculated by subtracting the estimated distance from a current positionof the coherence gate 64. In a case where the estimated target positionfluctuates by a threshold value or greater for a predetermined number oftimes in a row and continues to be away from a reference position of thecoherence gate 64, the controlling unit 40 determines that the coherencegate 64 is being driven in the opposite direction to the directiontoward the correct position. That is, the controlling unit 40 maydetermine that the coherence gate 64 is being driven in a direction awayfrom the correct position in a case where the number of times thefluctuation in the target position estimated by using the value outputfrom the learned model receiving the obtained tomographic image(estimated distance) and the current position of the coherence gate 64satisfies a condition of being equal to or greater than the thresholdvalue reaches the predetermined number of times. The reference positionof the coherence gate 64 here may be a representative value such as aposition of the coherence gate 64 optically calculated for an averageeye to be examined and an average value resulting from adjustments ofthe coherence gate 64 performed on many eyes to be examined can be used.In addition, the predetermined number of times here is set from thenumber of frames equivalent to a time period that is sufficiently largewith respect to a time taken by an average blink so that the driving inthe opposite direction is not confused with, for example, a blink. Forexample, in a case where an interval between every frame is 100 ms, thepredetermined number of times can be set at five as an example. Thevalue is not limited to the above and can be set at any other values. Inaddition, the threshold value for the fluctuation here can be set, as aninstability level, based on inference accuracy of the learned model oran inter-frame spacing of the training data and may be set at, forexample, a value that is sufficiently large with respect to theinference accuracy or the inter-frame spacing. As an example, thethreshold value can be set at a value that is three times the inferenceaccuracy of the learned model or the inter-frame spacing of the trainingdata. However, the value of the threshold value is not limited to theabove and can be set at any value.

In a case where the coherence gate 64 is being driven in the oppositedirection to the direction toward the correct position, the controllingunit 40 drives the coherence gate 64 to a position to which thecoherence gate 64 moves away from the rough-estimated position by apredetermined distance in an opposite direction to a direction in whichthe coherence gate 64 is being driven. The predetermined distance herecan be set at, for example, a depth range of imaging the tomographicimages. In this manner, in a case where a driving direction iserroneously estimated because images obtained at positions of thecoherence gate 64 away from each other have similar image structures orin a case where a tomographic image is so dark that a certain estimateddistance is continuously output due to being away from a DC component,the coherence gate 64 being driven in the opposite direction can bestopped in the middle of the driving and can be brought within a rangewithin which an accuracy of the estimation ameliorates.

In a case where the current position is away from the reference positionfor a predetermined number of times in a row, the controlling unit 40may determine that the coherence gate 64 is being driven in the oppositedirection to the direction toward the correct position. However, thismethod raises a possibility that the coherence gate 64 is erroneouslydetermined to be driven in the opposite direction when the correctposition is away from the reference position. Thus, by making thedetermination based on the fluctuations in estimated target position asdescribed above, a possibility of such an erroneous determination can bedecreased.

Other Modifications

In the embodiments and modifications described above, a case where thecoherence gate 64 is driven as an example of the optical path lengthdifference changing unit that changes a difference in optical pathlength between the measurement light and the reference light. Incontrast, another example of the optical path length difference changingunit is an optical head including the optical system illustrated in FIG.1, and the optical path length difference between the measurement lightand the reference light may be changed by driving the optical head inplace of the coherence gate 64. In this case, a driving amount of theoptical head can be determined by using a learned model from atomographic image of an eye to be examined. The learned model here canbe generated by learning tomographic images of eyes to be examined andrelative positions (or distances from optimal positions) of the opticalhead to eyes to be examined in combination. Note that any othercomponent or mechanism that can change the optical path lengthdifference between the measurement light and the reference light may beused as the optical path length difference changing unit. For example,the optical path length difference changing unit may include any mirrorthat is disposed on an optical path of the measurement light forchanging an optical path length of the measurement light.

In addition, training data for the various learned models is not limitedto data obtained by using an OCT apparatus itself with which imaging isto be actually performed and may be data or the like obtained by usingan OCT apparatus of the same model or an OCT apparatus of the same typeaccording to a desired configuration.

The various learned models according to the embodiments andmodifications described above can be provided in the controlling unit40. The learned models may be implemented as, for example, a softwaremodule to be executed by a processor such as a CPU, an MPU, a GPU and anFPGA or may be implemented as a circuit fulfilling a particular functionsuch as an ASIC. These learned models may be provided in an apparatusfor a separate server connected to the controlling unit 40. In thiscase, the controlling unit 40 can use a learned model by being connectedto the server or the like including the learned model over any networksuch as the Internet. Here, the server including the learned model maybe, for example, a cloud server, a fog server, or an edge server. Notethat in a case where a network in a facility, a site including afacility, an area including a plurality of facilities, or the like isconfigured to be capable of wireless communication, a reliability of thenetwork may be improved by, for example, using a radio wave within adedicated wavelength band that is allocated to the facility, site, orarea. The network may be configured by a wireless communication thatenables high speed, large capacity, low latency, and massive concurrentconnections.

The embodiments and the modifications described above can be combined asappropriate without departing from the scope of the present disclosure.For example, the controlling unit 40 can include both the learned modelfor the alignment of the coherence gate 64 described in Embodiment 1 andthe learned model for the tracking of the coherence gate 64 described inEmbodiment 2.

According to at least one of the embodiments and modifications describedabove of the present disclosure, an optical path length differencebetween measurement light and reference light can be adjusted with highaccuracy in optical coherence tomography.

Other Examples

Embodiment(s) of the present invention can also be realized by acomputer of a system or apparatus that reads out and executes computerexecutable instructions (e.g., one or more programs) recorded on astorage medium (which may also be referred to more fully as a‘non-transitory computer-readable storage medium’) to perform thefunctions of one or more of the above-described embodiment(s) and/orthat includes one or more circuits (e.g., application specificintegrated circuit (ASIC)) for performing the functions of one or moreof the above-described embodiment(s), and by a method performed by thecomputer of the system or apparatus by, for example, reading out andexecuting the computer executable instructions from the storage mediumto perform the functions of one or more of the above-describedembodiment(s) and/or controlling the one or more circuits to perform thefunctions of one or more of the above-described embodiment(s). Thecomputer may comprise one or more processors (e.g., central processingunit (CPU), micro processing unit (MPU)) and may include a network ofseparate computers or separate processors to read out and execute thecomputer executable instructions. The computer executable instructionsmay be provided to the computer, for example, from a network or thestorage medium. The storage medium may include, for example, one or moreof a hard disk, a random-access memory (RAM), a read only memory (ROM),a storage of distributed computing systems, an optical disk (such as acompact disc (CD), digital versatile disc (DVD), or Blu-ray Disc (BD)™),a flash memory device, a memory card, and the like.

At this time, examples of the processor or circuit may include a centralprocessing unit (CPU), a microprocessing unit (MPU), a graphicsprocessing unit (GPU), an application specific integrated circuit(ASIC), or a field programmable gateway (FPGA). Further, examples of theprocessor or circuit may include a digital signal processor (DSP), adata flow processor (DFP) or a neural processing unit (NPU).

While the present invention has been described with reference toexemplary embodiments, it is to be understood that the invention is notlimited to the disclosed exemplary embodiments. The scope of thefollowing claims is to be accorded the broadest interpretation so as toencompass all such modifications and equivalent structures andfunctions.

This application claims the benefit of Japanese Patent Application No.2020-208772, filed Dec. 16, 2020, and Japanese Patent Application No.2021-109337, filed Jun. 30, 2021, which are hereby incorporated byreference herein in their entirety.

What is claimed is:
 1. An optical coherence tomography apparatus thatobtains a tomographic image of an eye to be examined by using combinedlight obtained by combining (a) return light from the eye to be examinedirradiated with measurement light and (b) reference light, the opticalcoherence tomography apparatus comprising: an optical path lengthdifference changing unit arranged to change an optical path lengthdifference between the measurement light and the reference light; adriving unit arranged to drive the optical path length differencechanging unit; a determining unit configured to determine, using alearned model, a driving amount of the driving unit from the obtainedtomographic image; and a controlling unit configured to control thedriving unit using the determined driving amount.
 2. An opticalcoherence tomography apparatus according to claim 1, wherein thedetermining unit performs one of: determining, as the driving amount, avalue that is output from the learned model by inputting the obtainedtomographic image to the learned model and determining the drivingamount by converting the value that is output from the learned model byinputting the obtained tomographic image to the learned model into avalue smaller than the output value.
 3. An optical coherence tomographyapparatus according to claim 1, wherein the determining unit performsone of: determining the driving amount by, in a case where a value thatis output from the learned model by inputting the obtained tomographicimage to the learned model is equal to or lower than a threshold value,converting the output value into a value smaller than the output value;and converting the value output from the learned model by inputting theobtained tomographic image to the learned model into a value smallerthan the output value and determining the smaller value as the drivingamount in a case where the smaller value is equal to or lower than thethreshold value.
 4. An optical coherence tomography apparatus accordingto claim 2, wherein the determining unit determines the driving amountby adding an offset amount corresponding to an imaging view angleincluded in an imaging condition to the smaller value.
 5. An opticalcoherence tomography apparatus according to claim 1, wherein thedetermining unit determines the driving amount using a plurality ofvalues output from the learned model by inputting a plurality oftomographic images to the learned model, the plurality of tomographicimages obtained for one of different positions of the eye to be examinedor different optical path length differences.
 6. An optical coherencetomography apparatus according to claim 1, further comprising aselecting unit configured to select a learned model that corresponds tothe obtained tomographic image, the learned model being any one of aplurality of learned models that corresponds to a plurality of imagingconditions, wherein the determining unit determines, using the selectedlearned model, the driving amount from the obtained tomographic image.7. An optical coherence tomography apparatus according to claim 1,wherein the optical coherence tomography apparatus performs at least oneof alignment and tracking with the optical path length differencechanging unit, and the learned model includes a learned model for thealignment and a learned model for the tracking, separately.
 8. Anoptical coherence tomography apparatus according to claim 1, wherein theobtained tomographic image is a tomographic image that is obtained byperforming correction processing on an image obtained by using thecombined light, and the correction processing includes one of processingfor binarizing the image, processing for extracting some region of theimage, processing for generating a tomographic image including both areal-image part and a mirror-image part, and processing for generatingan image in which some image components are extracted from thetomographic image including both the real-image part and themirror-image part.
 9. An optical coherence tomography apparatusaccording to claim 1, wherein a target position of the optical pathlength difference changing unit that is a target for adjusting theoptical path length difference can be adjusted by an examiner.
 10. Anoptical coherence tomography apparatus according to claim 1, wherein thelearned model is a regression model, and obtained by supervised learningusing a plurality of training data that includes a plurality oftomographic images and continuous values obtained from different opticalpath length differences each of which is an optical path lengthdifference between the measurement light and the reference light.
 11. Anoptical coherence tomography apparatus according to claim 1, whereindetermining the driving amount by the determining unit and controllingthe driving unit by the controlling unit are performed after the opticalpath length difference changing unit is driven by using a result offocus adjustment for at least one of an SLO image, an anterior segmentimage, and a fundus photograph.
 12. An optical coherence tomographyapparatus according to claim 11, wherein the controlling unit performsexception processing in a case where the number of times that afluctuation in a target position estimated by using a value output fromthe learned model by inputting the obtained tomographic image to thelearned model and a current position of the optical path lengthdifference changing unit is equal to or greater than a threshold valuereaches a predetermined number of times, the exception processingincluding processing for driving the optical path length differencechanging unit to a position to which the optical path length differencechanging unit is driven by using the result of the focus adjustment. 13.An optical coherence tomography apparatus according to claim 1, whereinthe controlling unit performs exception processing in a case where thenumber of times that a fluctuation in a target position estimated byusing a value output from the learned model by inputting the obtainedtomographic image to the learned model and a current position of theoptical path length difference changing unit is equal to or greater thana threshold value reaches a predetermined number of times, the exceptionprocessing including at least one of processing for prompting anexaminer to perform a manual operation and processing for driving theoptical path length difference changing unit based on an intensity valueof the tomographic image.
 14. An optical coherence tomography apparatusaccording to claim 1, wherein the controlling unit determines that theoptical path length difference changing unit is driven in a directionaway from a correct position in a case where the number of times that afluctuation in a target position estimated by using a value output fromthe learned model by inputting the obtained tomographic image to thelearned model and a current position of the optical path lengthdifference changing unit is equal to or greater than a threshold valuereaches a predetermined number of times.
 15. An optical coherencetomography apparatus according to claim 1, wherein as the learned model,any one of a plurality of learned models that corresponds to a pluralityof target positions for the optical path length difference changing unitis selected, the plurality of target positions each being a targetposition that is a target for adjusting the optical path lengthdifference.
 16. An optical coherence tomography apparatus according toclaim 1, wherein as the learned model, a learned model that correspondsto a vitreous body mode is selected in a case where the vitreous bodymode is selected, the vitreous body mode being one of imaging modesincluded in an imaging condition, and a learned model that correspondsto a choroid mode is selected in a case where the choroid mode isselected, the choroid mode being one of the imaging modes.
 17. Anoptical coherence tomography apparatus according to claim 1, wherein thelearned model is obtained from training data in which input data is anew tomographic image obtained by shifting a tomographic image of an eyeto be examined in a depth direction of the eye to be examined by apredetermined shifting amount and that includes the shifting amount asground truth.
 18. An optical coherence tomography apparatus that obtainsa tomographic image of an eye to be examined by using combined lightobtained by combining (a) return light from the eye to be examinedirradiated with measurement light and (b) reference light, the opticalcoherence tomography apparatus comprising: a driving unit arranged todrive an optical member included in the optical coherence tomographyapparatus; a determining unit configured to determine a driving amountof the driving unit from the obtained tomographic image using a learnedmodel that is obtained from training data in which input data is a newtomographic image obtained by shifting a tomographic image of an eye tobe examined in a depth direction of the eye to be examined by apredetermined shifting amount and that includes the shifting amount asground truth; and a controlling unit configured to control the drivingunit using the determined driving amount.
 19. A control method for anoptical coherence tomography apparatus that obtains a tomographic imageof an eye to be examined by using combined light obtained by combining(a) return light from the eye to be examined irradiated with measurementlight and (b) reference light, the optical coherence tomographyapparatus including an optical path length difference changing unitarranged to change an optical path length difference between themeasurement light and the reference light and a driving unit arranged todrive the optical path length difference changing unit, the methodcomprising: determining, using a learned model, a driving amount of thedriving unit from the obtained tomographic image; and controlling thedriving unit using the determined driving amount.
 20. A non-transitorycomputer readable storage medium storing a program for causing, whenbeing executed by a computer, the computer to execute the steps of thecontrol method according to claim 19.